microstate_analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNVjT/z8G9/WDnAhr42ZCue",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ayushi15092002/mental-health-workload/blob/main/microstate_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qmOPYc_tCgFk",
        "outputId": "b5840dae-8708-48d7-8927-258ec82349de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install mne --upgrade"
      ],
      "metadata": {
        "id": "PzU7iooBoNZJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install mne-microstates\n"
      ],
      "metadata": {
        "id": "-UPQyifdobua"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import mne\n",
        "import mne_microstates\n",
        "import os\n",
        "from mne.datasets import sample"
      ],
      "metadata": {
        "id": "uJycVxwknkjd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_folder = \"/content/drive/My Drive/drdo/subjects\"\n",
        "\n",
        "fname= \"/content/drive/My Drive/drdo/channel_loc.csv\"\n",
        "# Read a montage from a file.\n",
        "TUB_montage = mne.channels.read_custom_montage(fname)\n",
        "\n",
        "read_data(clean_folder)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mGEuQ4kaeokn",
        "outputId": "61a3f0d0-d741-4d8b-b07f-e2241a5c3073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP004/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 691779  =      0.000 ...   691.779 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.0s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 12.4s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 27 iterations.\n",
            "GEV of found microstates: 0.845631\n",
            "Converged at 31 iterations.\n",
            "GEV of found microstates: 0.845212\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.846341\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845252\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.845258\n",
            "Converged at 21 iterations.\n",
            "GEV of found microstates: 0.846590\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845714\n",
            "Converged at 26 iterations.\n",
            "GEV of found microstates: 0.844847\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.836966\n",
            "Converged at 30 iterations.\n",
            "GEV of found microstates: 0.844847\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP004/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 691779  =      0.000 ...   691.779 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.6s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 21.9s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.3s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.8s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 27 iterations.\n",
            "GEV of found microstates: 0.845631\n",
            "Converged at 31 iterations.\n",
            "GEV of found microstates: 0.845212\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.846341\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845252\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.845258\n",
            "Converged at 21 iterations.\n",
            "GEV of found microstates: 0.846590\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845714\n",
            "Converged at 26 iterations.\n",
            "GEV of found microstates: 0.844847\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.836966\n",
            "Converged at 30 iterations.\n",
            "GEV of found microstates: 0.844847\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP004/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 691779  =      0.000 ...   691.779 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 18.1s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.1s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 27 iterations.\n",
            "GEV of found microstates: 0.845631\n",
            "Converged at 31 iterations.\n",
            "GEV of found microstates: 0.845212\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.846341\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845252\n",
            "Converged at 18 iterations.\n",
            "GEV of found microstates: 0.845258\n",
            "Converged at 21 iterations.\n",
            "GEV of found microstates: 0.846590\n",
            "Converged at 22 iterations.\n",
            "GEV of found microstates: 0.845714\n",
            "Converged at 26 iterations.\n",
            "GEV of found microstates: 0.844847\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.836966\n",
            "Converged at 30 iterations.\n",
            "GEV of found microstates: 0.844847\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP003/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 693419  =      0.000 ...   693.419 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 10 components\n",
            "Fitting ICA took 13.0s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.5s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.5s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (10 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 57 iterations.\n",
            "GEV of found microstates: 0.900304\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 34 iterations.\n",
            "GEV of found microstates: 0.898352\n",
            "Converged at 65 iterations.\n",
            "GEV of found microstates: 0.900527\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 29 iterations.\n",
            "GEV of found microstates: 0.900313\n",
            "Converged at 66 iterations.\n",
            "GEV of found microstates: 0.900160\n",
            "Converged at 42 iterations.\n",
            "GEV of found microstates: 0.900162\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900546\n",
            "Converged at 56 iterations.\n",
            "GEV of found microstates: 0.900297\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": 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pCWXF129obGbj5i/ZW1vb4ZCNar8OeCBdfzv1toO9ZXX23Q8vrD578viCMcMGQ4I3vGOEJ3juiSayQ+PSfG5Khdr9B3jw6cXWOVMnnjjxwtkb023XqUFrDD2tadGSd9Z9vWuvw6ayY0sc4iOrM6R0pX2i+rV1+3lr1dotp140J/mYToAuvYNnff7uQ8C1ib4J6FZ0j/w1wBRjyIRO9UpXvz65Aeitlf3D+BvdSlaCKOwkPgPONYae1mlBXX6d1fr8XTfwBjCjSwLi0OoP0OoPkJ+bHXP9MIj+EphkDD2tSx9hHNYL0FblCjfwAhAbOeluPUbhFw88xu5v6nju/tu6bE8UNgLTzeOm1HZVwGG/Mm9VrjBwHjKvof3bH2lj555vOHiokdHHDkxcIX2ylwEXm8dNOazvCbrt6xOrcsVU4Cmgb7cI7DwOIeV/mMdNeao7hHXrZzlW5YpslP1rYA6HET3t0PE8swyYbY6YWtNdKo/IF27W3945HZgPjOt24dGQxjZgvjli6jPdLrq7BQKYo6atNEdNOxkYCzzl8/tb7nrkOeoO1HdZpm3b3Pv489Tsq7WBJcAMlD34SJACRyhi4vEvs6YN/vCzL1579PbrmiceP2KwVnZ+x60chJZr38FDTZXTZv8srzAv97bl6za8cuSsDen9rv8bSPDTtwTQHyeaxgLDAC/Oj+wS8IXKdpyd/E+ALa4xMxP8Bnvk8J0T8/8FR7/UT4KjxCTBUWKS4CgxSXCUmCQ4SkwS/B2+PUK4DDbGmAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAABGCAYAAABxLuKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVf0lEQVR4nO2beZQVxb3HP7V03302mAEGARlEBGVxixuiBiLGJdEQE/UledHEJGbfNR4NeRpNYhJ9J5r4NCExC1GzG6OBxAXEHTAIKAKy7wMzzAwzc7fuqvdH973TM8wMi2DeO4ffOXW6b/VWv09/f7+q29UtrLUcsb1N/rsb8H/VjoDpw46A6cOOgOnDjoDpw46A6cP023UhIUSVM+nqVmAUcLLUzsnKTZwstTtOCJM2u1Yn02PfZZ14KqfcRFa77hrtqMXalYukkouVlq8vuf3CDNBi34YxxtsCRo+Y8j6c1CyzZ5uWmSFpqR2kdhFSIR0X07KRWP0EhJQAqbAMBE4zxiKkxetsz7nV9cWKhhPvOeWbc29edMt0/3C2WRwu+KmzvpgyXuFD1vjXGd+baFs3IKtGIHUsAKJdpHbBeohcC/H6E1CxONpNILWLdl20o5BaoHQQ8R3rFpEeMR4VS24C7hdS/PSVWy/YcTjaf8jBVJ/9WYWTvDG3demNsuKouIhVYk3XzRVSlYt0XPJvPk1yzDScdA3K7QKjtEI7CiFBaYmQAiFE+Txhu4vAT62x1y//wSXth9KPQwpm5LW/P87LtT/gFbKneZ17yO9YAdKh2LIZXTsGhAaTR0iH4pZX0DUjiA0+HuXEULE4SrsoN4HUDkpLlJLdwABYYzHGYo0NfwOwwVj7sVV3X/rkofLlkIA56eY5qpj3vlLM+f/lFf14MZfFK2QxXgFTLFJs3wnGkN/+OkI5xOonIpQOwslxEVKG4dOlFiFFGYyQAhmC8T2DNWBsAKcECrDWmPuElF9be9+Mt6yetwzmHTf9rkYlav7iFc3ZxbyHV/Qp5H28go9fyGG8Ar5XwHjF7heWsiukpKLQtA6b20N69OSyQqQQ3cAYYzGexfdNRDkGa3ys8UvnW6e0eveb975v5b8NzLQ7Hh/csn3DMwhndLyuoRsUr+jjFQ1ewcd4RYxXKOcaIVV5WVrf89rj+J27qT37ml6hQKAW37cYz+B7fhlI2RmpkNpBu2qnduT01+98z7/edjDXPPhKXbbgP5Mt+GPWv/QkHbt3MuSMSwlUY0IwPr5n8IoG45mS5MtWclhIUc4hUSDRfaB/MCUoSku0I3FiujWWcM5b+K3zDwrOQYG54bHXq7IF7+lswZ/UWfApeIbOvMe65x7DWkX66ImseeRuBk++CrdmWBlQkBN6NEBG10U3ECKybo3tBsYr5Ng1/ydkxp5PfPBYhJRRKLgxjZvQO9vXvnzh0p9+Y9GB+nhQfwkycf1gOu5MSsc1mbgmHddkEg6jp1xCRf1wCi2bcZIpElWVODFFLO4QSzg4MY0TV92KdiPFUUgly0UI0VVCVUkhkFqilEZn6tCpmqBrd1UZinYVTkwh/M5aWWh/7EO/XpQ+UB8PeOR730vrP56O6wuUFJSK6xm0FLha4o6dQHtHlo4tq9AKlCuwjsL4Bt8Le5H9UKntEXa+Z8KBnkEYkPEYdZM/AoBSwSBQKokb0zgxhd+xi9yudYx956V1mbj+PnDdgfh5QKH0P0/8a0S8asDSorEVec+QLfqUQilb8IKQKvhkw9LespuVj/2cgePPJT18XFc4Ra7ZM+8EUPYG1K0nsrYbOCEF2lEoJXFiCiem2frMQ0y65MPUVCRJx7VNuHrabe8e+9RhAXPLzx5+oW74qNMztYPJe4acZygaQ7bQHVC24JP3wvq8R7azg40L/oqKJYkPGsmOhX+n4ZLPIGSXYHsCKjm+ed5vidUMpXrs2eU6EwHTFV4BnMKudWS3rea4qe+jMulQlXRIxx2SrlqfcNX4L5zVsF9jnP0OpQVrd119xpTzTv/rQ7/CNzDl0iuRAopGoEQQRtmCH4STDuCoMLxijiJ5wVV0NDey881ldGxZRcfm1yjs2Y31PQaOPxuvrZlYRQ3CSXTBsRbtaJyYgxNT3YBFTUgBXhavrRm/ZSvjpl5GJoRSmXRJuoqEo46Oafk94DP74+9+KWbp1lZR9M3qnGdG5T1De2cHTz72CKMnnUb1kKPIh8rJhSopqSWqnkJYfGPxQ+eKhQLZ5h3odDWNSxeAdohVDqB5xUIGnHAWbRtWUGhrZsgZl7Dthb+Rqm/Arails3EDVaMm0fzGyxQ7Whh8yjR2LZlHw+QLqRk0pNwhVCZdMnFNXCviWhLTsrBtzRsNX33/tC2HBMyK7W0XeMb+3TM2CCHfsKezkzl/eJCTp15EIlNF3jcUw+295Z6CZ8qASmD8yN3va71kpRFuIZ+j0N5CsnIACIEbT6CVDJUqycQ1CVeRjgeKiWtJTEniWrH4qcdRmJ9898uf3Kdq9gvMm41tf/UtlxSNxVjIeX45xyxfspinHv0zH/jM18GNkSsB8KPq8bopxgtV0xuAvsCoyJgm+ltJQUzLctgmXE3SVaTjmqQTKKV583pemTeHK679LAnX2RhXsuH0o2v6fZ6zTzCbmttH+Nau9Q3SMxbfWjwTwCn6gULa9uxh9coVqFiCQSOP3Ssxl5SSLXhlIP2B6QmhZ110qKBkF5RYqJpS6GhTZMf61expauSsc6aSTsaJa4mj5KUT6isf6c/vfSZfR4prpUVKCJMtCGDHjm08MfdxLv2Pa5CVFRw/8UQeuv9upg4aQiyZCRwwXY7o0JFANaZPMFEldNXJbvXR85WgKCFwlMCREiUsi+b+hWUvPsM1X/w6p55yCnEVgHOUQAnxKaBfMPsc+WrJNC0FWhIM4pTAkfDG8ld59ok5SOORdCSZuMuHP/0lWrZv5h+z7yeuBDElSTqKlBvEfa5pK4/feQPenmYSrt6rpONOeVkqFXHN0/fdSuPrLzEg7QYl5VKddKlOOlQlHDKupjKuyWjJ9pWv8ud7vsNrL85jzNhxjGoYRcZVpFxFTAftd5U4Z2NTu+rX7/42Lnj2OT1h0okTpQ3UIg1ICxLBhRdfwvSLLsYzQWgpEYwnxk+YhJSSnRvXkMhUkqqpJS8MSkJVRZqqmgFUVaSIxXWfiokWCdQMGEjtgBoyMR1eJ9hPCoGfz2GNx9zZvyLuusy4+pOcecYZOFIQ1xJXSVR4U5UQKAmOFAklxDhgWV++95tjZs+efc7GjZvmffwTn8CNxfEt+NZS9C2+pZxEi4YQUFev1dLaxkP3382J557PiOMmUDSGom/LS9/uHUrlcAnDQgpRBuGoIFwwPq8tfI7Wpp0MHX40y156lnddejlDjhqOq1UZiKNkWeURIOV1JcTVNZnkAwcFJtfees269Rtm3T/rF0yYMJHL3n85VoiwZwkhGYsfgumt18oVPX5x520c/47JHH/alCBph/+X/PBJHASPG5SkGwwlg5yBNSx7/mlW/mshF191NY1bNzHmhAlkUmmkFCgBjgzyRwmIEqU8tBcQlIBsZ8e9g+tqP31QYPItO3+MkJ+2SvPCy4v4/R/+yOUf/CAnnXwqviWE0jukgh8oI+cbCp7P1i2bWbb4JSpr6xk18RRMqDwIRrjPPvowTdu2cNm1X8B1XZQQCAx/uu8uBg8dxgUzriCmdRlEGaAMoJQglBzXQZINoETqN23cwMMP/pZ0KrX2+htuGHVQYApNW+cg5HQrFQiJLySzH/wda9at47rPfp6KqqownPpWT9Evdd2WfNFj3py/MXTUGF575WUmTp6KEIIffOljrF+xFCEkmcpqrvj89Wxf/ybvuuwKhg4bRiqRLPc4JRBKCgQgBN3VEIEQ9kBILC8+/xxz5/ydo0cM54rLZ1BVkdkZy1TXHRyYxvXzEXIKQoLUWKlAaXY2t/Kje37MiIYGrrzqQyBVn7nHt13hFeQYS9E3rF+7hs6Odu6e+TWU1nz1+/fyzz/OJt/ZyUtPzeWBf7yA6zhlIKUwEVB2XoigW1U9Q6W03Rqe+OdcFsyfz+SzzuTid09HWQPWIIzf7tYMzvTle7/d9Xuv/M8Ttm7eiPAL4BcQXh5RzFNbmeLWb97ISRMmMPOmG5n7+KM4AuJa8uKCedzw+etQGOJahN22IOkoMjFNxlVkXM24MceSjrusee1Vbr/3V6x48RmSrsuXZt7Oji0bmfW9maRjirSrw65W4khBTAfDgJgWxJXkJz/8Lr//9S/C+mB7Z1srv5x1P7fMvJlMPMYd3/4W750+FRX6YAtZPvm5L6T6873f7rq+bmA+7ShEMQ/SAymxUgePC4zPSccfy0m338K8Bc9z8403cOo7TuPohgaGDxtGKqYxVgThZS3aCHxrcaTC1xZrISGCEaAuZhk5Ijim0g3uVTIRJ+PqIAmLLlWIyG8pYPhRQxkyZAj59jaefvIJli9bSkU6zeUzLmXksKPAeIhiLnjIYzyENUivyFGDak1/vvcbSv7ahU8j5LlWasrhJAQojZUawlIKsRcWLuaxv8/FjcWYcs55nHzKKUjthF0z3ZbGBkl35g1fYeumTdx21z2kUkl+dMd3WLJ4Ib/72xwcpVGSMHyCvGF8jzWrV7HqjTdYu3YN+VwOAaTTaaa98xwmjhuLwCKMD8Yrw8D3ghCyNqiHNj18fOXBgVn53N+EUhdZIQIIIlBMsFTdAZXqpCZb9Ji/4FleXrgYz/fJVFRy+hlncsLEiWjHxbdgbdBVFwoFvj3zZh6e/WuKxQJTz5/O9+/6b+pq6yjk86xeuYJlS5eyaeMGJOA4mtHHHMO4sWM4ZuTRxF03UEPUaWsQ4RLfCyCVc4tXekS4TTWcWn9QYLxlT94BfA0pEdpFKEWph7JRtQTzqNhQVWVIwTwILW17eP7lhSx5dSn5QhGEIJOpoLaujng8jlf0aG1rpblpF8ViMehtgFjM5bhjRzNpwnhGDB0SKMHaspNABEpYZ8rJtTuIsM764Z9q4z+pxk6Z1pfv/eYYm+tYTDghhi5ipUJoB6RC6ACMKEHyi+G6AimD9RBMdUJz0blncdF5Z5eBtbW309i4k1w+j+NoMqkUAwcMwHV0190P8wLWIPJtZafDOxppaC+QSkA8D0xpDspA1wTd4v587xfMPxa89MbytRupyaQ5c9I4jm0YgdVOMHtYWpYghRCiQES4tEKUJ5CEDOoqHUnl0IGhk6Fjfkfw9z0KpqcioiB6rAtrA0WUJuJCEN3W/UMA5oUly5d+bsb5rc1t7ZXzFy7hgUf+waCBNbz7rFMYOWwo23a3Mrx+MEK7gUqiU69KlYGIoJLV6zbyjTvu5s6Z1zOsfnB3KHvd9cBJYwwf+8ZtzHjXFC6aclqwj9n7GVN5qtYE52nc1YT1fTZs3c7SVevZuKMRrA1mOoXgmKGD3vzo6Zf16fs+H1RlH73nKeA8AKSisT3L3IXL+cvz/+KVN9bx5avew6QxDRzXMJzBtQOCd1jCcCpBKh3b1NLKnb/8A9dfexUV6R7DiIjMo3d4d0srt/z0IcaPGk5FKknrnnay+QK5QhHP8/H8oPSgxJ/nv8yAigxfvHw640fWM2xAVRka0JK46Lrq/vzeJ5iO33/vixhzV+CcLIdRZ8HjtY07GHXUYJZv2MKqLTvZsbstPGuoFAGu41CVSVGZSpJJp0gn4qQSMRLxBForPM9nT0cHO1va2NbYxI7m3Rg/zCNY0okYw+pqGFSZpqYiTSYeIxlziTkaVyu0CuaTykxCSCvWb6YyGWNITWU36KH9Jjnjqx9+S2D2/HJmlTVmC5AEkI5GKFVWRSnPdFOI7HoGVPANLR1Z2jqytOXydGTzdOTydOYLeMaipSCTiDOwKsOQ6grqMkmklF25AaJ3uruDketE68uKC+ttD0UJpc5MXXnTC28JDEDTPV+bBVwTnFQG77aU5pfD9fCCXY2V3f9tCNnvA7Muh6B7WPUMk/6OD5VmjSmrLloXvvy4pPqT3zlxX+farwm3Ykfux8A11jcBmBAKUAZTAgaUQQHl/aImekCzEUX0dKjn9t7OU9puI8f2ds6wLffu02EOYIp20zc//pI15h3RBkklA5XQHVCkEb060psZY/F9H61k2ZEDsd6g9BJCrcDQ+pvu7djX+fZ7ira4p/MbwBPGNyLqdPdQ6lo3wD3Pvsr7J45maGW6V1BR+/5Ti9ja2s4P3zNln23JFT1+/NxSPnrqWAakgilda0w3OCUrrYdtu23Edx7YJxQ4ADANd/32qW9PPvGxbR3Zi6215VdLU45mQDJObTLO4EySulSciniMrDEs3bSDEwdUMtD2rqCoTR8+mJ3tWfIte8+59zy2uSPL8i2NbB0+iIra6l7ziW8sed9HAHGtkEq9KJT84f76e0BvO8y/bFr64VXrN54zpK762IoM1lpy1tCcL7Azm2dnocCufJ49hWIwrdDzYqJ7nQCiV7fWlut67gvB40wpBUmt0FJS8A2dRa/83LjsiwkGcjEl6Sh6LGpsNucMGzT1zsUr5u2vrwf8qtmwVPLxz4xuuOCdg+r2arlQ4XtzKvK62D6Uss8GRgBLJfCMIev7+MaipSSpVRC39P4mxNr2Dm5cuHTZ6rb286212/f7ugfzDt6zkyf/CPic9Xt5JaMXOAdqPZN01zl7h9xfspZKLhBKnHfq408e0LcHB/WRhZf1vmSNrTe+mQEQBSRU9A73r5Ye3Wg3iyqlJ5jS7+h1o3Ai+y2VSrznzPkLDviDjIN+nXXu6JNc4BHjmwt6kzB0d65kfe1bsIaCtWR0172KgigpUPSixN5ujFRypZDinHMXv3hQH2G8pRegH60/wTXG/haYUWqbH55P9ZI8o9t72m/ad9BkPL5QMbR8bImBlCIcM/UfniLcD1gilJh+/huLGw/Up/K53uor8w/VjlO+tXcBn/Utoi/HSxZNS9HM0GSKZI3PcB0vAynND0XXewPeA+AcocQVF65/tfXgvTqEX5/8rPq4qQZm+daOgC4Axlp8gm69yRTZbTxarEenNfiR7lkJSApFtdDUSIfByiURJuFg0oxewZTqlKAN+PLlja/POhT+HNLPcr6bOSa9yc/NWuV1fsBSGqcIFBBHkhGaSqGpEArXqh5OWjrxabM+rdajyRYoYpHAIOUyTqeoVkH+KR1XggXMUUJ84iNNKzYdKl8OyxdutyZGvVMKcbtvOS0aWj3zUNSikKLhI7E047HGdNJhfRwhGKkSjNFJlBBvKsHt17Ws/MWh9uGwffoHcFNs1Em+tZ/2rLnyeb81eaKsINbP5GfPpBv9LYBn/d1MUhm/Q/gLmmzxN63G+/ncQtNhceCwginZOJE5djv5P51HTUe9iB8LVPW2X0/VRMBki5hls71tlUmhblrjd/7hcLf5bQETtU+JowXQAJwclnFASkKCIG1klSALrFFCLFaCRUqIFXcU13pvZzvfdjD/X+zIl/p92BEwfdgRMH3YETB92BEwfdgRMH3Y/wLIYA4u+srDpwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP003/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 693419  =      0.000 ...   693.419 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.0s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 10 components\n",
            "Fitting ICA took 9.6s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.6s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.6s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (10 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 57 iterations.\n",
            "GEV of found microstates: 0.900304\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 34 iterations.\n",
            "GEV of found microstates: 0.898352\n",
            "Converged at 65 iterations.\n",
            "GEV of found microstates: 0.900527\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 29 iterations.\n",
            "GEV of found microstates: 0.900313\n",
            "Converged at 66 iterations.\n",
            "GEV of found microstates: 0.900160\n",
            "Converged at 42 iterations.\n",
            "GEV of found microstates: 0.900162\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900546\n",
            "Converged at 56 iterations.\n",
            "GEV of found microstates: 0.900297\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEICAYAAABcVE8dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAN/klEQVR4nO3cX4il9X3H8ffH3WxD/BNLdwJhd5O1dK1ZbKl2sJZAY9GW1Yvdi5SwC5IaxAVbQ6k2YEkxYb1KpSkEtjUbIjYp0ZhchIFs2AtrEEJWdsRG3BXDdGPd2QScGCMUUbPttxfn2Dkddz2PM8/MWef3fsHAec75zTlffsy858z5l6pCkrT+XTDpASRJa8PgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS0sk2ZHktST/OulZpD4ZfOmtDgLHJj2E1DeDL41Ishf4JfDopGeR+mbwpaEklwAHgDsnPYu0Ggy+tOhe4KtVNT/pQaTVsHHSA0jngyS/B9wAXDXpWaTVYvClgeuA7cALSQAuAjYk2VlVV09wLqk38eORJUjyPuCSkbP+hsEfgNuramEiQ0k98x6+BFTVq8Crbx4n+S/gNWOv9cR7+JLUCF+lI0mNGBv8JA8keTHJM+e4PEm+lGQuydNJfIJLks5DXe7hPwjsepvLbwR2DL/2A/+88rEkSX0bG/yqehz4xdss2QN8rQaOApcm+WBfA0qS+tHHq3S2AKdGjueH5/1s6cIk+xn8F8CFF174+1dccUUPNy9J7XjyySd/XlVTy/neNX1ZZlUdAg4BTE9P1+zs7FrevCS96yX5z+V+bx+v0jkNbBs53jo8T5J0Hukj+DPAJ4ev1rkWeKWq3vJwjiRpssY+pJPkIQafM7I5yTzwOeA9AFV1P3AYuAmYY/BOxU+t1rCSpOUbG/yq2jfm8gL+sreJJEmrwnfaSlIjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjDL4kNcLgS1IjOgU/ya4kzyWZS3L3WS7/UJLHkjyV5OkkN/U/qiRpJcYGP8kG4CBwI7AT2Jdk55Jlfwc8UlVXAXuBf+p7UEnSynS5h38NMFdVJ6vqDeBhYM+SNQVcMjz9fuCn/Y0oSepDl+BvAU6NHM8Pzxv1eeDmJPPAYeDTZ7uiJPuTzCaZXVhYWMa4kqTl6utJ233Ag1W1FbgJ+HqSt1x3VR2qqumqmp6amurppiVJXXQJ/mlg28jx1uF5o24FHgGoqh8C7wU29zGgJKkfXYJ/DNiR5LIkmxg8KTuzZM0LwPUAST7CIPg+ZiNJ55Gxwa+qM8AdwBHgWQavxjme5ECS3cNldwG3JfkR8BBwS1XVag0tSXrnNnZZVFWHGTwZO3rePSOnTwAf7Xc0SVKffKetJDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIwy+JDXC4EtSIzoFP8muJM8lmUty9znWfCLJiSTHk3yj3zElSSu1cdyCJBuAg8CfAPPAsSQzVXViZM0O4G+Bj1bVy0k+sFoDS5KWp8s9/GuAuao6WVVvAA8De5asuQ04WFUvA1TVi/2OKUlaqS7B3wKcGjmeH5436nLg8iQ/SHI0ya6zXVGS/Ulmk8wuLCwsb2JJ0rL09aTtRmAHcB2wD/hKkkuXLqqqQ1U1XVXTU1NTPd20JKmLLsE/DWwbOd46PG/UPDBTVb+qqp8AP2bwB0CSdJ7oEvxjwI4klyXZBOwFZpas+Q6De/ck2czgIZ6TPc4pSVqhscGvqjPAHcAR4Fngkao6nuRAkt3DZUeAl5KcAB4DPlNVL63W0JKkdy5VNZEbnp6ertnZ2YnctiS9WyV5sqqml/O9vtNWkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhrRKfhJdiV5LslckrvfZt3Hk1SS6f5GlCT1YWzwk2wADgI3AjuBfUl2nmXdxcBfAU/0PaQkaeW63MO/BpirqpNV9QbwMLDnLOvuBb4AvNbjfJKknnQJ/hbg1Mjx/PC8/5PkamBbVX337a4oyf4ks0lmFxYW3vGwkqTlW/GTtkkuAL4I3DVubVUdqqrpqpqemppa6U1Lkt6BLsE/DWwbOd46PO9NFwNXAt9P8jxwLTDjE7eSdH7pEvxjwI4klyXZBOwFZt68sKpeqarNVbW9qrYDR4HdVTW7KhNLkpZlbPCr6gxwB3AEeBZ4pKqOJzmQZPdqDyhJ6sfGLouq6jBweMl595xj7XUrH0uS1DffaStJjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktSITsFPsivJc0nmktx9lsvvTHIiydNJHk3y4f5HlSStxNjgJ9kAHARuBHYC+5LsXLLsKWC6qn4X+Dbw930PKklamS738K8B5qrqZFW9ATwM7BldUFWPVdWrw8OjwNZ+x5QkrVSX4G8BTo0czw/PO5dbge+d7YIk+5PMJpldWFjoPqUkacV6fdI2yc3ANHDf2S6vqkNVNV1V01NTU33etCRpjI0d1pwGto0cbx2e9/8kuQH4LPCxqnq9n/EkSX3pcg//GLAjyWVJNgF7gZnRBUmuAr4M7K6qF/sfU5K0UmODX1VngDuAI8CzwCNVdTzJgSS7h8vuAy4CvpXk35PMnOPqJEkT0uUhHarqMHB4yXn3jJy+oee5JEk98522ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktQIgy9JjTD4ktSITsFPsivJc0nmktx9lst/Lck3h5c/kWR734NKklZmbPCTbAAOAjcCO4F9SXYuWXYr8HJV/Rbwj8AX+h5UkrQyXe7hXwPMVdXJqnoDeBjYs2TNHuBfhqe/DVyfJP2NKUlaqY0d1mwBTo0czwN/cK41VXUmySvAbwA/H12UZD+wf3j4epJnljP0OrSZJXvVMPdikXuxyL1Y9NvL/cYuwe9NVR0CDgEkma2q6bW8/fOVe7HIvVjkXixyLxYlmV3u93Z5SOc0sG3keOvwvLOuSbIReD/w0nKHkiT1r0vwjwE7klyWZBOwF5hZsmYG+PPh6T8D/q2qqr8xJUkrNfYhneFj8ncAR4ANwANVdTzJAWC2qmaArwJfTzIH/ILBH4VxDq1g7vXGvVjkXixyLxa5F4uWvRfxjrgktcF32kpSIwy+JDVi1YPvxzIs6rAXdyY5keTpJI8m+fAk5lwL4/ZiZN3Hk1SSdfuSvC57keQTw5+N40m+sdYzrpUOvyMfSvJYkqeGvyc3TWLO1ZbkgSQvnuu9Shn40nCfnk5ydacrrqpV+2LwJO9/AL8JbAJ+BOxcsuYvgPuHp/cC31zNmSb11XEv/hh43/D07S3vxXDdxcDjwFFgetJzT/DnYgfwFPDrw+MPTHruCe7FIeD24emdwPOTnnuV9uKPgKuBZ85x+U3A94AA1wJPdLne1b6H78cyLBq7F1X1WFW9Ojw8yuA9D+tRl58LgHsZfC7Ta2s53Brrshe3AQer6mWAqnpxjWdcK132ooBLhqffD/x0DedbM1X1OINXPJ7LHuBrNXAUuDTJB8dd72oH/2wfy7DlXGuq6gzw5scyrDdd9mLUrQz+gq9HY/di+C/qtqr67loONgFdfi4uBy5P8oMkR5PsWrPp1laXvfg8cHOSeeAw8Om1Ge288057AqzxRyuomyQ3A9PAxyY9yyQkuQD4InDLhEc5X2xk8LDOdQz+63s8ye9U1S8nOtVk7AMerKp/SPKHDN7/c2VV/c+kB3s3WO17+H4sw6Iue0GSG4DPArur6vU1mm2tjduLi4Erge8neZ7BY5Qz6/SJ2y4/F/PATFX9qqp+AvyYwR+A9abLXtwKPAJQVT8E3svgg9Va06knS6128P1YhkVj9yLJVcCXGcR+vT5OC2P2oqpeqarNVbW9qrYzeD5jd1Ut+0OjzmNdfke+w+DePUk2M3iI5+RaDrlGuuzFC8D1AEk+wiD4C2s65flhBvjk8NU61wKvVNXPxn3Tqj6kU6v3sQzvOh334j7gIuBbw+etX6iq3RMbepV03IsmdNyLI8CfJjkB/Dfwmapad/8Fd9yLu4CvJPlrBk/g3rIe7yAmeYjBH/nNw+crPge8B6Cq7mfw/MVNwBzwKvCpTte7DvdKknQWvtNWkhph8CWpEQZfkhph8CWpEQZfkhph8CWpEQZfkhrxv4KLgyAWLznAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP003/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 693419  =      0.000 ...   693.419 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 10 components\n",
            "Fitting ICA took 8.4s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.5s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.6s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (10 components)\n",
            "    Zeroing out 0 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 57 iterations.\n",
            "GEV of found microstates: 0.900304\n",
            "Converged at 38 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 34 iterations.\n",
            "GEV of found microstates: 0.898352\n",
            "Converged at 65 iterations.\n",
            "GEV of found microstates: 0.900527\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900543\n",
            "Converged at 29 iterations.\n",
            "GEV of found microstates: 0.900313\n",
            "Converged at 66 iterations.\n",
            "GEV of found microstates: 0.900160\n",
            "Converged at 42 iterations.\n",
            "GEV of found microstates: 0.900162\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.900546\n",
            "Converged at 56 iterations.\n",
            "GEV of found microstates: 0.900297\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGIAAAD8CAYAAACMyXE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAHZ0lEQVR4nO3dXYicZxnG8f/Vxg+ItYUmgthqBNPGIELroIUeGGiFtAf1QBEDxQ9C90RFsAiKUkWPakFBqB8rhmjBaO2BLLjSA40UpCmd0FraSCXUr6iQNA05KVYLtwfvRNbtzs673Sc7V2auHwTm42HmCf/MvDMD7x1VFTF9l017A9FJCBMJYSIhTCSEiYQwMTGEpEOSTkt6esz9kvRtSSclPSXpxvbbnH19XhGHgf3r3H8bsHv0ZwH47ua3NX8mhqiqR4AX1lnyQeDH1TkGXCXpza02OC+2NXiMtwB/W3H91Oi2f65eKGmB7lXD9u3b37Nnz54GT+/t+PHjz1fVzknrWoToraoWgUWAwWBQw+FwK59+KiT9pc+6Fp+a/g5cu+L6NaPbYgNahFgCPjb69HQTcL6qXvG2FOub+NYk6QiwD9gh6RTwFeA1AFX1PWAZuB04CbwIfPJibXaWTQxRVQcm3F/Ap5rtaE7lm7WJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECZ6hZC0X9Kzo3kbX1jj/rdKOirpidE8jtvbb3W29RmKcjlwP93Mjb3AAUl7Vy37MvBgVd0AfBT4TuuNzro+r4j3Aier6rmq+jfwU7r5GysV8MbR5SuBf7Tb4nzoE2LcrI2VvgrcOToPexn4zFoPJGlB0lDS8MyZM69iu7Or1cH6AHC4qq6hO/n9AUmveOyqWqyqQVUNdu6cOCdkrvQJ0WfWxkHgQYCqehR4PbCjxQbnRZ8QjwO7Jb1d0mvpDsZLq9b8FbgFQNI76ULkvWcD+gzOehn4NPAw8Ae6T0fPSPqapDtGy+4G7pL0e+AI8InKiOUN6TWvqaqW6Q7CK2+7Z8XlE8DNbbc2X/LN2kRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmGgyAmK05iOSTkh6RtJP2m5z9vX5j8UvjID4AN3J7o9LWhqdN3dhzW7gi8DNVXVO0psu1oZnVasREHcB91fVOYCqOt12m7Ov1QiI64DrJP1O0jFJ+9d6oIyAGK/VwXobsBvYRzcO4geSrlq9KCMgxms1AuIUsFRV/6mqPwF/pAsTPbUaAfELulcDknbQvVU913CfM6/VCIiHgbOSTgBHgc9X1dmLtelZpGmNzBgMBjUcDqfy3FtJ0vGqGkxal2/WJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYaDaLY7TuQ5JK0sRzxuL/TQyxYhbHbcBe4ICkvWusuwL4LPBY603Og1azOAC+DtwL/Kvh/uZGk1kckm4Erq2qX673QJnFMd6mD9aSLgO+Cdw9aW1mcYzXYhbHFcC7gN9K+jNwE7CUA/bGbHoWR1Wdr6odVbWrqnYBx4A7qmr2xwo01GoWR2zSxFFyAFW1DCyvuu2eMWv3bX5b8yffrE0khImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhIkmIyAkfU7SCUlPSfq1pLe13+psazUC4glgUFXvBh4CvtF6o7OuyQiIqjpaVS+Orh6jOxc7NqDJCIhVDgK/WuuOjIAYr+nBWtKdwAC4b637MwJivD7nWU8aAQGApFuBLwHvr6qX2mxvfmx6BASApBuA79ONfjjdfpuzr9UIiPuANwA/l/SkpKUxDxdjNBkBUVW3Nt7X3Mk3axMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTLSaxfE6ST8b3f+YpF2tNzrrWs3iOAicq6p3AN8C7m290VnXZBbH6PqPRpcfAm6RpHbbnH19Tu9daxbH+8atqaqXJZ0HrgaeX7lI0gKwMLr6kqSnX82mLzHX91nU6zzrVqpqEVgEkDSsqsFWPv80SBr2WdfnranPLI7/rZG0DbgSONtnA9FpMotjdP3jo8sfBn5TVdVum7Nv4lvT6D3/wiyOy4FDF2ZxAMOqWgJ+CDwg6STwAl2sSRY3se9LSa+/p/IP10O+WZtICBNTCTHpJ5NZIOmQpNN9vytteYieP5nMgsPA/r6Lp/GK6POTySWvqh6h+wTZyzRCbHR86VzIwdrENEL0Gl86b6YRos9PJnNny0OMG1+61fu42CQdAR4Frpd0StLBddfnJw4POVibSAgTCWEiIUwkhImEMJEQJv4L9xQ1uKlxrVcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAABGCAYAAABxLuKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAU2ElEQVR4nO2ceXxU5bnHv+97zpmZ7BtJTELYYlkEAYu4xBUUrKCtlohLrW2tvRVLuS5464YLIlXqUlurCFVwAy51qdYFFFABFZUIiAqySiBhCyHrTGbOOe+5f5zJzCSZbCz23vvhwfOZc973Pcvzy+9Z54zCcRyOSWuR/+4H+N8qx4BpQ44B04YcA6YNOQZMG3IMmDZE/75uJIRIv2LuZzX1jVZRwLSHWSF7WChoDTOD9glBfyCpfNncpPSTLnMsy2k0G+sDVqBhq9VYXxqsqyo1a8rXKzO4xt70rxSg2vkecgzxfeQxF900bdyHLz7x97wThicWjSzxJHbvT32jRci0MYMWoaCF/0Al9Xt3YZsKmdYTM1CLFWjAaqwnWLOPxs1LbFVbHpCpBX/VCounhNY8ax/NZz5qwCxcV57kN+2r/aaa4A+ZQ9Z8+B55A4dTuXc3e3dsp2LTVxSeM46g8GHaglDQIug3qdr4KYHKcvScQSAMLNvGbKihsfYAwV2rcaQXLa1wJzALmB1a8+zeo/H8RxyY9RU1WlVtw+R5c2bde1yv430Di0fgN238psJUipDlbv7GIPX+AB//Yw56ajbJvQcTMpJQwocZtKjbu4d9pe+Q2OccTCUJ1R4g1FBDY9lnKOlFJGYjpGYCs4E/hNY8W38k9TiiwJQfrO8fsp25jZZzqqkc1n25nl3l5WR170VqbncaLRvlgO04KMfBtB1CtqKu0WJ32Q7WLnuLnCFnU11Th5bdh0B9kNqKcvaWLsZbNBKzvpqQvwbTX0eo7BMQGiKtJ5j+Coyka82v5i8+UrocEWDefedNbc/+qkc2btr8u1//doLuSUolaClCyqGmPsBzs59kxKVXoSem4AiBJoQLjnIwlYNpK4K2orbRoqYhyCdvv0LA1kgZdC51tY3s/aoUW6RgOQahhlqsxnqsYAAr6Ec11uA07EckZjnSmzJb8ybdUvfhjMNmz2EDE9pfloHUX3c046y9B2t49PG/UXzW2Zx93mgabUXQcgjZDhs3bea1+c9TfP6FbP92A/v3VDD28mv414LnKDy+H97kNA5WVTFoxBiqAyaVdUHee+EpsopLqK+3qNm1k4qP/kHKoEswzRB2MIAdCqCUjaNcPyykhpTadql7Lqx887Zv/23AWLs25DpCvIumD3akDpoHR/fyypvv8PWGb/ndTbdgC52Q7RAMg6TC95NCRK7TaJpMv/1mNq5fx33PvoJt+KgKmGzavI2N679E73MqDfVBavcfoG53BaYpEd5krFAAZZooKxQBRkgNaRj7DV/yBWXPXbPmewfG/m5NjiPkcqAfUseRGmg6ThiczWUVPDFzFnfcfR+6L5Gg7TJHhW8no7igHLCUg78xyOatW/n0k4849aLLqW602FlZy9uzH6Vw9C+pawR/tZ+dH8wjoVcxIiEbZTbiKBsVZo2me5C6B92j1zTu/Pynu958aNn3Boy97fN04H1gKEKCkDhCgtRxNIN9NQ3cdOd9XD/heha8/Bp33H0fvqQUQraDHb5fDGGwFTiOg6nAVA4frViBkZpOYk4PqoI2m7aV8d22bZhJuQREEv76IA2V+9ny4h9IHfQjUgdcgINESoEy/dg1ZQhMkrLyqtJ6FhV/fPt5XTarQyoJhLLnC2UPFcpG2CbYIURkM0nUoXteLr0L8rjnjtuYPvUegv46vLrAo4U3Gd18usCjSXy6u3/W2WeRk57KK09MJ9UQ9O3Tg8EnD2f38oVotTtJSvWRktONjP7FZBQNxtq3lvovX0anlsCWZSSkp5M98HQyeh+f6UvyvHPx058kd1nHrjLG3rD8OtzcISpSQ2gajhCun9E8OJqBY/hwjAQO1Pm5d9p07rhzCsnpGThAE2EcXFNywmHcVhCy3Yi2YdNmDjYEScwppDqk2F3tZ8umzdQELELJuYQaLSzTxjJtHAVCgpQCqUl0Q8Pj00n26iT79Jkv/vzkCV3RU7v33ns7vXjO/bcOXP75un8KlL5hy3Y++PQLyvfsIz8rDUMTCAQIx1VaaGHtBQkJCRSfcQbTpk2jX7/+ZGVkoIXDthACKQSaFEjhOmUh3GtlZGZSW13Fwll/ZehpZ6IbOp6kFEpfe5aM3AIS0jIQuguC4dHQdA3Do2P4dBJ9Bsk+nWSPoGzF68PeWP3tV1eMGL6hs7p2iTFm6VuLyyurRn+xYQtpyUn0yMthz/4q/rX8U8744WDGjihG6B4wPBEnjO7B0X04upeggv+6/U5Wr17Ngw/NYNjJJ2MYRsQhO46D7bh+xlKus378kT/hS83g+B+ejje7gKpwKD9YXcvKBU+TfcKppBYNxVZRPTy6xNAEles/Iqd7IQkJiWQX9vrOo8sT7xnVr1M5TqcZE/r45V/hODenJiXQr1chPfNySE9OontuN847ZQiLP/4cISTdj8tGSOl6V6EBwv1PSDTDIC8/n/KKCvJyc3nl5ZdZsXw5VZX7KcjPJyEhIcwgN2oJYPVnn9K9sJB9FeXUVR8kO787Hl3D5/PSe+hpaMKhdscGyle9TW5BIfvWfkD11rWkpSTi0wX9hp5MZlYmSV493Wdo6af3zHy7M/p2ijHB5fMFsFlIrQgpIz4lVhSCu558gT9OntCMNW5uY7is0QyXQeFIhpCYls28efPolp3NmDFjXH8D2OGsOGQ7BCyH+pDN8o8+Jq/fYIIKgpbCb9oETJuQpQhaCstW+GsPkpCcis/rwatLPLpEk67ZGpoIGVL2uHZ4jw4Lz85FJWVfgLKLHGWDUqBsHDOEY9s4tps/SCnxGoY7D+AohKPAUeFzLISywbYQtoVQFigLQ9cYM2YMixcvpq6uLvoXC/sdTQoM6Uatot59eOnhe7FrD5Dq08lIMMhK9JCR6CEr2UO3FC89Co4jOy2R9ESDVJ9Bkkcj0XA3ryY9Xl1e1xmVOwmMuiEMEBFwwseoMDgqTnukCRhHRYAQyo6AJRwXsOysDKbcdRd33XUXBw4caPZwmgBNgiGhsCCPSbdPIVFzWDr/GbRgA6k+ncwEg8wEg3SfQYpXdzePTqIhI6BEwNHlf7z21W6t9cN2EZjgsud7AmNjwWgGTuRKGspRrc53meO4650Y5ijLZU8YuOysDKbdfz/T7p9KXW1t9LLh6KVJN//JzkinqFcvLr50HJtWr2THl5+jGg6S6tVJ9emkhUFJDjMlwQUjsvk02cOry4sOGxhH2b+JrIsBo2zPPp56+R1ifZSUEmScSzaxJpZBjuPux4CTmpzI3VOm8MC0+905XB+uSdDDwPg0gU+XFBYUcPEl4zih/wBWvPEP6veWs3fLNyR5NJI9Gmvef4ddG9fj02QTU/CFwTGkuP6wgQHOd1qaibJZ8+023ly5mpBpRYYFgvZk+44d/HLCJCoqypuDo6KgdcvK5GdXXcXMJ59EkwKU4tYbJ/Hxh+/j0QS6Fs2evbqge14u1028kcKCAip3bmf9yqWULnubRQvmsu6jpezZ9i0NB/ajAn52b99CTeU+lr22YOStDzzSbr+73cnGJXN0YEi8uR+fNZyfnHNaM4Y4TSajbISj4YRZ4n5KEr1eumVmkOD1RBmBxFEWoCMkOMripKFD2LhxI+8tXszIUaPplp1NVkY6mhAgo39N4bghXQiJkZLIRZeWuNmzbTNo8FAysrrx8QdLqawoo2fRD9ix6RtOPOlkxo67wqPr+gBgfVu6txuuG5fMGQysiyyWWhSIcImPlAjDA1Jj6qx53DPxWoRuIAyPW3FLHUfqbqEZrsDdfT0Ssh2pRfaR0bk7p9zNzbdMJiUtHdtxkz5bRUsH99NNCt2uoFtaNIV7INLmiCO/GlaYMbetyY5M6eQO5kFGHbwQbfsXIOqE443Hhvbw/n9OmsTsWU+7CR+EnXDLT4EmXB+kCYEuBboEQxMYmhvmjfhbu7q1D4yyh0WUjgEgli2RpS2jVDyAYgARMceOspn7wktMnf4gpmlG/E1Odjc8Hg87y8rcvEbEB0fXwgmcjDpqQ4rIGl3SLJsOb8PaetSOgYGiDuYjsruqmrzsrLhgxEYjoBkr9u3bx4ixlzBp8m08+pe/MWDoML5c92Xk/Ot/+1tmPvWkq5QQbpSKBSX8aUgXoAgg4WjWVKxGtjB4mmxft/aBkVpC8+M2lkuNPZVV5GVnRjEJZ8QRMML7EbMJAzbx1js4ZdhJVO/aSnX5du6/+07GX/0LbDMEjiIxwcfIESN47dVXIyYVC46MB4IkUgZoIhryZfi88L/munUFmEvu/POg8sqDrUCIJwKBUoqlq0r59R1/xLIsF4QIIDGmBKAU67/+htffXszkidcz58V5TJvxCFdeNo7vduzg1j/cEVl/wehRfLd9G1+UrnbbEzHgPPLQA8x7fg5auPh0A5eIgNEEUFMWLQQIFFNunph0yMDkZ6UHk3ze5v4lniibosJ8tpSVk5OZQffjstGaiswWjIGosw2G3CZ2TU0NBXm55B+Xi7LcvMjjMRC2FTG5yTffxOJFi/h01aqIskJAQX4B+fn5URbFsKcJIClcgESMSeUXFLTrFDsK1+8D50aAiROqI5W21Lhn5otMnXSdG8KlFu3sxYTjZmFaCCbdNY2yXRXMfOwhkpJTmTrjUT4r/YIlb72OphvuuqYQj+DRPz/OCScMZNQFF4TDsxuuleNEuoG2cvch2h1UODSpGo7ktUXZKWlt6d6R823oYL6Z6JpGwO9vXUfFOt8WZcHD99xGUa8eDDx9JPn9B7OjbCcLn3sGXcroujBzBA633HQjFeW7ePWVVyIOWWvq/BFlhxZz3GReTSwL+5t2deuIMTOE1G6NDHTAmA3f7eKjtRv4zfiLW7NGiHACpzfrxzQleI4QKAek4W0+13ReDHMQknnzF4AQjL/iyjaZ48QcEzMWlqU9spLPb0v3jhhT2sF8VJTNgF7d2VGxhwNVB0GpcBXutiWidZHVPHw7CqHceU0Ktz0Rr9iMYQ6O4qorr0DTNP57/rxmzNFahusW7NGamouifd3aBUZIrWNgVLRZBTD5FyVMnz0vUjfFBadFpd0y620XnJjxyy8rwTAM5r/0UgScpoglW5qSiALkmt5hAANsBWrclc2XWpZN2e59rUBKS0rgJ+eexvw3l7Sa27T1O0pu+AM7y3e33YZoAY6yLX59w+95e9G7rdoUOIrxJeNISPDx/HNzm4FTWbmPgN/fiinutxEC0YE1tAuMd+Q1DvBFvMRu3rsruOz2PxEyTYBmrDlr2Ims+3YLwcZAlDVAZloq/Xr3IC05sf0eTUxmLHD4QZ/e9CosiNvDwVGU/PRSumVmMvvpmRFzum3yZB5+cHozU9JEhEHV+RnJW9vTvcNmePCDF28EHotC6YZuf9Dkq21lnHpi/8hYkxMG+HLLDtZt2s41l/wofF7XnDEy5qvflvNN52rNz1267H1Wl5Zyy+Rb2bhxI6np6eTmHkdTjGxyxsCL3VKTft6e3p1pVM0F/C0HkxJ8nDqwr3sQ28gK7w/u25tvtu6I3wuOlVjWxIxFTKqNMN+ywYWjOG/kCEaPGsWdd9xOjx49yM/Li5hWJFt2TenJjpTuEBjvuVdXAwvc1R32kJvJCUU9+GrLd+5BW444VtryN/EcddP6FuCcNHQIN914I/fcPYWv16+PmFZT1ithbWZK4ieHDUxY/tYZUGL9DEox7rwzeG3JyvZZ09LXdFJa9nBiwTkuN4eHZzzEypUreOzRRwj4/dG2gxBPdeb6nQLGe+7VXwCfdfqpw0AkJvgIhkJYVsdvniqlsCyrS6yJB07T1zSaFPxuwvVcVlLCg3+czksvPI9lmjVS8FJnVOjKayC3g5tEdlhUArZt88DseQw6vhdLVn0RGW8rr7n3sae4/rb72vQlseAE/H6mPvgn9u/f3xwc22rFnp49Cnng/qkMGTyYv8+etSA5MaFTZU6ngfGefeUy4OmOQGkyp0AwxNqNW8nNSmfZZ2vCX861bSrjx47iqkvGtBqPB05NbQ1r13/Fnj17WjMnDntwFEOHDF418YYJN3RW3y697RBauTB52pyXyy46Y1jG0L693cHYHCc2bDcdS8nzb7zH0AE/YMiAvpH1cUO3iB+io6E9fK82wjgQXRtet+/AAR5/4ilr7JgLh484f/TazurapTeqPGeOr5+3ePmqbRV7nVagtCVKccWF5zLvrWUdsqZJWtVSsaxpY01cv+MoKvftZ/G7724YOeqCPV3R9ZDewQutXPgX4PdAXMZAC9YAz7+1jIHH9+bkEwd0jTXuwtbMiTCkOWPisGcFQo7wpmV16bcHXWJMjNwEvNJqNCYsR0J3eOxnY0bw0ptLccIOsqW0/Eu3Gb5jSwbiNNqd2DnnSxz1466CAocIjOfM8TZwFbCo1WQbOYsmBBefcwr/XPZRZCw2QjUGg1TX1jU7J65JRe7TBjjR7VscNdqbkVt9KDoeKmPwnDk+BPyEeMwJS0vWjDhlKJ+s/ZrGQKAVa+5+bBYT757ROruNXKyFD4H44Lhr1+Kosz1Z+Yf8y5TDf2V+5UINKR8DJkLMt/ot/U34eFvFXt74YBU3/fLy8Dq3uNyxez/V9Q0MGdgvjm9p7UeaRZ/mPmcRcIUnp1fN4eh1yIxpEs+Z421PcckkYBSwIzIR5w0JgD7d8wg0BqnYE+3lOMqmZ142Q/oVxa2hWvkRiMecWhx1nSen14WHCwocwd9EeopLlgKDgJlAMxq2rKEmXvljnlzwevvhuwOTgmbgLEKpQcZxxz9zpPQ5YsAAeIpL6j3FJROA84FP22JNSlIiOZlpcTuAkVKhhcRtTcAW4Fojv++FRn7fnUdQlaPzK1pPcckyT3HJabhfnD8TCIb8U2fNZ//BMMOV4uqLzmPBO+9Hjlu9nATNWGPbNg/8ZRY7yytsHPUG8CMc1dco6D/naOhwVH9F6yku+QK47rqxI2d89vWmV0cOH9yQnZXZF0jPSE2h3h9wWRSn/hKO4zpYVwK19Q3r/7loadqKz0rvWrJi1ctH87nD9/9+/28g5udvCKTWBymHLVz84cUlo89J1wxPElImCKlJIIDUAkLTtjpClCLkamCD1me41dG1j6R878D8X5Gj4mP+P8gxYNqQY8C0IceAaUOOAdOGHAOmDfkft8c97EEEJWAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP002/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 704799  =      0.000 ...   704.799 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 27.0s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 2 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 71 iterations.\n",
            "GEV of found microstates: 0.621273\n",
            "Converged at 78 iterations.\n",
            "GEV of found microstates: 0.624490\n",
            "Converged at 63 iterations.\n",
            "GEV of found microstates: 0.620620\n",
            "Converged at 45 iterations.\n",
            "GEV of found microstates: 0.619922\n",
            "Converged at 66 iterations.\n",
            "GEV of found microstates: 0.623552\n",
            "Converged at 39 iterations.\n",
            "GEV of found microstates: 0.620843\n",
            "Converged at 52 iterations.\n",
            "GEV of found microstates: 0.620884\n",
            "Converged at 89 iterations.\n",
            "GEV of found microstates: 0.624249\n",
            "Converged at 77 iterations.\n",
            "GEV of found microstates: 0.620200\n",
            "Converged at 93 iterations.\n",
            "GEV of found microstates: 0.618853\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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mqPEmvjCmppy5oMYerK95z0BEpmGteUd4eCiBrqLW/VBObhEyGtQgpGEo+7+0vo4PCDm7Rft1Th8Cgw/RpHuDYhVVVU7trUHYsC646jbDnR6Ju0Y+71xyCq42rRCEE4lZhF3QDkSQwJOQHExgWMV3Pbf9kUlc7zj2J2QQ1SKIdWtcjLvkHL5enE7WypepmGUevEx2xTh+7J7SaiBRVS8qT1qV1gGhqq8BrwHExsZqjYCnipSvcS55H0aP6pPNBccLaEUAGE1ogyBc7pJ1HWsOxlrJrTAiTsfXs4/TXF2iPMpL147w4hywvs6HuJ7Q6tkDdFmokF3YfahiBNUsXqZs3Ia1vl65Zxf7wpgSsZZTyqUZDq/qExjiJjCk8ncLKhK32012FfiHvC+xFwoaBTwG3FHe9Hwxsv8FaCsiLe3VfyZhLQJpcBC320228T+Ei8hqj2NavvjnsJZ/duROeb1lUtUsEbkFa11yN/Cmqm7yth7VHcuY/Ktlch0OIuSNFh4hOw4XNp1IRC4CDqpqvL28WLnxyZhJVb/AWrjdUEH4ozGVkr7AGBEZibVOex0ReUfLsUOI+QdONcWMmYpGVe9V1WaqGo011PimPIYExpiqLWbM5H0qrWvcUD5MN6/kqOoKrP2hyoVpmaopLpf1aHOMQXkNY0zVGLdLzLjJixhjqsbUDHJz7FT12C62KmCMqRpzbssIfk04XrygwRGMMVVjYrt1Ys3uVF+r4TcYY6rGxPaKIz7hhK/VcITNiZW/hTXGVI3pef5I4nce9bUa5ebAsQyGP+3ITkQVijGmakyrrj05kZ7F/qNl3azR96gqN7/3B5NH9/e1KsVijKka43K56N66Eb8mVM1xU1pmNre+v40jacKs1z/ztTrFYoypmhPTtSNrdlf+8UZ+Vm0/Su/ZaziYHswX38UTXKOWr1UqFjOdqJrTo1ccb72x3tdqlIq5P+xj1uKdPDfrHiZMfyhvNkdxnEqpxcYFvT1CVlSIfoVhWqZqTuyAC1mzq+o4IdbsPs7MhTv4bvlyJt46o8SGVBmoOpoaykSLzr3IzM5hb0rVcEL87ePtPH7vrbTtHudrVUqNMaZqjssdwHmtG1WJcdPahOPsPpLG1Xc+5mtVyoQxJj8gpltn4quAMb3900GumzCCwKCCNzuo7Bhj8gN69O7HmoSTvlajWJZuPMzYK2/0tRplxhiTH9Bj8Gjid6ZQmffi+vPgSdIylXP7DvW1KmXGGJMf0KxdV0KDAvjz0Clfq1IoyzYnM6xPV0Sq7uqzxpj8hLjO0az80/cu8vV7Uvl5x7GzwpdtSWH4yDE+0Mg5jDH5CXFxcaza6dtpRQnJaYx4YR1j5qxn/Z7TupzKyGbltiMMmzDVh9qVH2NMfkK/ISP50cct079/PsSEYb2ZcdsUHvni9N6O3287SpcWYdRvGFnE1ZUfY0x+wrkDRpJw5BTJJ323kP/STUmMufxarrv7SX7ensLvBywP4+frkxh9wUCv6iIiUSKyXEQ2i8gmEbmtvGkaY/ITAoNDiW3TsMDxijc4ciKTjYmpDLzoMmrWDWPauAE8/80esnOUhesOMe6qG4pPxFmygDtV9RygNzC9uE33isMYkx8RF9uVldt9Y0z/2ZpM/25tCA0NBWD6/bP5eM1BXvsukajw2rTvWdS+Pc6jqvtUdY39+zjWtjJNy5OmMSY/ou+Awaza4RsnxNItRxk5fHjeeeN253HzhKHc/emfPDX7SZ/olIuIRAPdgZ/Kk475BMOP6HPBOOJvvo/M7BwCS7jZmxPk5ChfbjjIrDfP9NY9Nm8Jj8zNwu12phqmHKvBgq+7eQaFi8hqj/PX7E308hCRWsDHwO2qWq5m2xiTH1GvWRuah4WyIfEE5zWvXfwFDrEm4ThhdUJp2aHrWXFOGVIhFLqlDICIBGIZ0ruq+kl5MzPdPD9CRIjr3NLrLvKlm5IZ0b/QOu0TxJpqMRfYoqrPOJGmMSY/o0+fOFbt8O4M8gXrDjN6/OVezbME9AWuAgaLyFr7GFmeBE03z8/oN2QkD7/wttfy27z3BEmpGQy4eLLX8iwJqvo9Dm9Db1omP6NNzwtIz8gmITnNK/m98cN+Jo3ohzsg0Cv5+RLTMvkZrqAaxLVvxKrtx4iKCanQvOJ3HeOD1ftZv25JheZTWTAtkx8SF9O1wp0QySczuWLuFv756N9p0qpTheZVWTDG5If0GXQhq3ZWrBPi/s93cWHfbky8rWqu51AWjDH5IT0uuJSt+1JJTcuqkPS3HTzJwnUHefKNjysk/cqKV41JRGaISKJTrkhD2QgNa0rX5vVYXUGLrMxdeYCrxgykfkSTCkm/suILB8SzqvoPH+Rr8KBPTBd+/HM3A9vVdzTdnBzlvZ/2sfw/3nO/VxZMN89PGTLyYpZtSXY83dW7jlOvVggdYgc4nnZlxxfGdIuIrBeRN0Wk0NeiiEwTkdUisvrQoUPe1M8vGDL+WrbuS2Wfw9vNLN54hFGDqt5qrE7guDGJyNcisrGAYyzwMtAa6AbsA54uLB1VfU1VY1U1NiIiwmk1/Z7gGrW5MLYti9YnOZruFxuTGDP+CkfTrCo4PmZS1RItfCYirwOLnM7fUHLGXTqBua/N4fr+zqy9sCspjX0pafQZNcmR9ErLAUnn2cAdpwO8vNG8t715nu6dccBGb+ZvOJORV93Cqj+TOXrKGRf5ko1JDO/ZnoDAIEfSq2p4e8z0lIhsEJH1wCDgf7ycv8GD2vUj6Ne5BUs2OtPVW7wpmdFjxzmSVlXEq8akqlepahdV7aqqY1R1nzfzN5zNuHHjWLih/F69g8cz+GVHChdecZMDWlVNjGvczxl79XS+3nyYtMzyDTDmfLuXcf27Ujfcv/5R64mZNe7nNGzWki7R4XyzNZmRXcJLde2CdYd4b/VhMrNy+HnHUX5Z9VEFaVk1MC2TgTHDh7B4U+m6et/+nsxt72/j4kmTmTx1Kj9+v4Lm58RUkIZVA9MyGRh12RSen/cxqm1KvAvFzMW7+cfMu7jitkcqWLuqg2mZDHToOZigABcbEk+USH5DYiq7kk4x4eYHKlizqoUxJgMul4uRcZ1L7CJ/9b/7ue6yUQT66f+TCsMYkwGAUWPHsWRzyhlhm/eeYMycjcxelpC362DKyUw+it/PtL/P9IWalRpjTAYABl16DZsTj5GUau2SkZGVw6Q3NnP+kAv5ZF0yz3ydCMDbqw5wYWxbIlv7x6fopcE4IAwAhNRtyPldoli2OYkrejbmheWJtGnemPuef4crb1pLXFxvVHN4cfkeFn8y39fqOoKIDAeeB9zAG6parkXPTctkyGPixMt544cD7ElO49mvdvPcq/MAaN6hG4s++YDvE7J5/K6biBlS9acMiYgbeAkYAZwDXG62lDE4xmW3PER6thD7+GrumDyatt375cV1HzSGJfE7ueb+53yooaP0BLap6nZVzQDmA2PLk6BU5u3scxGR48BvvtbDQcKBw75WwkG8VZ4Wqlrox20istTWJZcQwHO1zbxdMERkPDBcVa+zz68CeqnqLWVVrqqMmX4rajeDqoaIrDblcR5VHV68VMVhunkGfyURiPI4b2aHlRljTAZ/5RegrYi0FJEgYBKwoDwJVpVu3mvFi1QpTHl8jKpmicgtwDIs1/ibqrqpPGlWCQeEwVAVMN08g8EhjDEZDA5RqYxJRIaLyG8isk1E7ikgPlhE3rfjf7K3nK+0lKA8d4jIZntRzv+ISAtf6FlSiiuPh9ylIqIi4nN3uVdR1UpxYA0C/wRaAUHAOuCcfDI3A6/YvycB7/ta73KWZxBQw/59U1Uvjy1XG/gOWAXE+lpvbx6VqWUqyfSOscBb9u+PgCFS0k9DvU+x5VHV5ap60j5dhfW/jspKSaffzAJmc+bMA7+gMhlTUyDB43yPHVagjKpmAUeBMK9oV3pKUh5PrgUq836VxZZHRM4DolR1sTcVqyxUlf8zVWtE5EogFjjf17qUFRFxAc8AU3ysis+oTC1TSaZ35MmISABQF3B25XnnKNF0FREZCtwPjFFVZ7ekcJbiylMb6AysEJGdQG9ggV85IXw9aPMYuAYA24GWnB7gdsonM50zHRAf+FrvcpanO9agvq2v9XWiPPnkV2AcEL5BrTFQ7vSOLViGsklEHhGRMbbYXCBMRLYBdwCFumd9TQnL879ALeBDe1vScs0Nq0hKWB6/xkwnMhgcotK0TAZDVccYk8HgEMaYDAaHMMZkMDiEMSaDwSGMMVUQIhJmu7vXish+EUm0f6eKyJwKyvN2Ebm6iPiLRMRsW1FBGNe4FxCRGUCqqv6jAvMIANYA59n/EypIRmyZvnp6gq3BIUzL5GVEZKCILLJ/zxCRt0TkvyKyS0QuEZHcTbSXikigLRcjIt+KSLyILMu3a30ug4E1uYYkIn/1+FZqPoBab84VwEVeKayfYYzJ97TGMoQxwDvAclXtApwCRtkG9SIwXlVjgDeBxwpIpy8Q73F+D9BdVbsCN3qErwb6O14Kg5k1XglYoqqZIrIB6wO8pXb4BiAaaI81gfQr+9MtN1DQLvVNsKb55LIeeFdEPgM+8wg/CEQ6WQCDhTEm35MOoKo5IpKppwexOVjPR4BNqhpXTDqnsJYDzmUUMAAYDdwvIl3sLmCILWtwGNPNq/z8BkSISByAiASKSEGbI20B2tgyLqyP9JYDd2N9qlLLlmsHbKxwrf0QY0yVHLU+ER8PzBaRdcBaoE8BokuwWiKwuoLv2F3HX4EXVDV3W8BBgF9+CVvRGNd4NUJEPgXuUtU/ColvBPxbVYd4VzP/wBhTNUJE2gONVPW7QuJ7AJmquta7mvkHxpgMBocwYyaDwSGMMRkMDmGMyWBwCGNMBoNDGGMyGBzi/wH7sxwfN5VKpQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGIAAAD8CAYAAACMyXE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAHZ0lEQVR4nO3dXYicZxnG8f/Vxg+ItYUmgthqBNPGIELroIUeGGiFtAf1QBEDxQ9C90RFsAiKUkWPakFBqB8rhmjBaO2BLLjSA40UpCmd0FraSCXUr6iQNA05KVYLtwfvRNbtzs673Sc7V2auHwTm42HmCf/MvDMD7x1VFTF9l017A9FJCBMJYSIhTCSEiYQwMTGEpEOSTkt6esz9kvRtSSclPSXpxvbbnH19XhGHgf3r3H8bsHv0ZwH47ua3NX8mhqiqR4AX1lnyQeDH1TkGXCXpza02OC+2NXiMtwB/W3H91Oi2f65eKGmB7lXD9u3b37Nnz54GT+/t+PHjz1fVzknrWoToraoWgUWAwWBQw+FwK59+KiT9pc+6Fp+a/g5cu+L6NaPbYgNahFgCPjb69HQTcL6qXvG2FOub+NYk6QiwD9gh6RTwFeA1AFX1PWAZuB04CbwIfPJibXaWTQxRVQcm3F/Ap5rtaE7lm7WJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECZ6hZC0X9Kzo3kbX1jj/rdKOirpidE8jtvbb3W29RmKcjlwP93Mjb3AAUl7Vy37MvBgVd0AfBT4TuuNzro+r4j3Aier6rmq+jfwU7r5GysV8MbR5SuBf7Tb4nzoE2LcrI2VvgrcOToPexn4zFoPJGlB0lDS8MyZM69iu7Or1cH6AHC4qq6hO/n9AUmveOyqWqyqQVUNdu6cOCdkrvQJ0WfWxkHgQYCqehR4PbCjxQbnRZ8QjwO7Jb1d0mvpDsZLq9b8FbgFQNI76ULkvWcD+gzOehn4NPAw8Ae6T0fPSPqapDtGy+4G7pL0e+AI8InKiOUN6TWvqaqW6Q7CK2+7Z8XlE8DNbbc2X/LN2kRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmGgyAmK05iOSTkh6RtJP2m5z9vX5j8UvjID4AN3J7o9LWhqdN3dhzW7gi8DNVXVO0psu1oZnVasREHcB91fVOYCqOt12m7Ov1QiI64DrJP1O0jFJ+9d6oIyAGK/VwXobsBvYRzcO4geSrlq9KCMgxms1AuIUsFRV/6mqPwF/pAsTPbUaAfELulcDknbQvVU913CfM6/VCIiHgbOSTgBHgc9X1dmLtelZpGmNzBgMBjUcDqfy3FtJ0vGqGkxal2/WJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYaDaLY7TuQ5JK0sRzxuL/TQyxYhbHbcBe4ICkvWusuwL4LPBY603Og1azOAC+DtwL/Kvh/uZGk1kckm4Erq2qX673QJnFMd6mD9aSLgO+Cdw9aW1mcYzXYhbHFcC7gN9K+jNwE7CUA/bGbHoWR1Wdr6odVbWrqnYBx4A7qmr2xwo01GoWR2zSxFFyAFW1DCyvuu2eMWv3bX5b8yffrE0khImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhIkmIyAkfU7SCUlPSfq1pLe13+psazUC4glgUFXvBh4CvtF6o7OuyQiIqjpaVS+Orh6jOxc7NqDJCIhVDgK/WuuOjIAYr+nBWtKdwAC4b637MwJivD7nWU8aAQGApFuBLwHvr6qX2mxvfmx6BASApBuA79ONfjjdfpuzr9UIiPuANwA/l/SkpKUxDxdjNBkBUVW3Nt7X3Mk3axMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTLSaxfE6ST8b3f+YpF2tNzrrWs3iOAicq6p3AN8C7m290VnXZBbH6PqPRpcfAm6RpHbbnH19Tu9daxbH+8atqaqXJZ0HrgaeX7lI0gKwMLr6kqSnX82mLzHX91nU6zzrVqpqEVgEkDSsqsFWPv80SBr2WdfnranPLI7/rZG0DbgSONtnA9FpMotjdP3jo8sfBn5TVdVum7Nv4lvT6D3/wiyOy4FDF2ZxAMOqWgJ+CDwg6STwAl2sSRY3se9LSa+/p/IP10O+WZtICBNTCTHpJ5NZIOmQpNN9vytteYieP5nMgsPA/r6Lp/GK6POTySWvqh6h+wTZyzRCbHR86VzIwdrENEL0Gl86b6YRos9PJnNny0OMG1+61fu42CQdAR4Frpd0StLBddfnJw4POVibSAgTCWEiIUwkhImEMJEQJv4L9xQ1uKlxrVcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEYAAABGCAYAAABxLuKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW3ElEQVR4nO2ceXhV5b3vP+/7rrX2mJ0BQkJCEiBAGMIYBBERUGQQvVY96rGtA55qW8/T9uppa231VKyttbZH7bW9x+nIdR6rKIqAgtShKCKTzEPIAElIQuaQ7L3XWuePtdfO3hkQGbz3Pg/v86xnTXtlvb/P+v6+77D3irBtmzOlZ5H/tyvw/2o5A6aPcgZMH+UMmD7KGTB9lDNg+ijaN3UjIURayzN3N9mmVQiU2JZVYplWCTDaNq0A4AOEbVkdwFFgH7AB+FxIuUEouT39pt+lAI32N9DH+EbAPHbzty7P65f65Nqte7UZIwcHLdMCwHbXlhX/bAxSAOgPTBVKYlsWTe0dHUMHpEdmjRrySPNTd90VWvQb83TWWZwu+I1P/CoAfBf4oWXZ45dv3s0FxYV4pIyDsLsBcot7Xkgn04WSIAWrtpUyfVQBKV5PhVTyMSHl4ynXL645HfU/5WDqH/mZEkreBtwJhLqft03LWSwLy7SwbZuy2kb21jZwqKGFpo4wUkBWKMCM4XkMSAmgDA0hJTK2FkoilURIGQEeB25PuX5x66mM45SCqX3w1pHAEmAqdD1xcJ66C8QMR2lobeepf2ylIxxhcEaIwoxUBoYCpHoNLNumqrmdNfsqQQp+OLME3avH4Uhdc8G4f7sM+Jfgd3/9/qmK5ZSAqfrdvyrg34DFQklv/I8rp+JSdQEyw1GqG1p4aPV6bj1vIuleI66ipIopRxlba46wel8lP517NppHd6DoGkJJlK4lfFbZwKPAzwLX3HnS6jlpMNX3/yjdNq2ltmXNsG2bvbWNDEoL4jP0ZI+IFdu0WLzsQ26dMQGvlHEo6ytqqGpuY+GI/LgahHIUsuFgLR8eqOLnF52DZugor5GYTi4Y9xalwILANXfuOpm4TgpM7YO3Zpnh6ErbtMbZlsXz67cRMS0aj3bSFo5QkB7i6pJRaAmy31RRzZ7qei4dORjbtLAiUSzT4onPd1Dd2s4vp49P8hGpa0hD49OKGmqOdnL1tLEOGCnj3pMI3q2aFYnOCy36zcZvHEzDo3cMMDvCfzcj0SI3wJc+30FhvxATcwcAsLPmCM9t3MUv50wh6Hcy7P6V6/jx9PGomFLMSDTuPYnpJGIwlesphsbvP9jAz+afQ0qKv1evcUvMx5psy5qd8cP7TwjOCfV8W59dnCaUXCENrUjGvEBIyZWTRvLOzjI+Lj3Iv762GikFPz53An/9eDO2aXKkpQ2PptBjTzjebMeg9FhiRm1FoljhKFeNG85L67djWxZtRzu5+fGlrNtd7lwfW8xI1L0m1ewIr6i+/0dF3xgYodQLStcmCCm7npqSKE1x57xp7KlroqKxlRc37uLpDTvIDPoAWLO3knlFBcf82/EguzfrpsXQjBDlR5qIdETQgJzUABleD2Y4Gl+sSNdihqOZNQ3NKx68bFbG147x66ZS+9KHv2eHOx4nGsHs6HQq1BGOP92utOjZMf28vIbS+kYuLy7EcoOIpWF3EEkGrGsoQ0N5DVbtqyS3fxqThuaiYq1TEtgEpdU1tfLH9z7j5zMnPj76gWdv/jpxfi3FdLz7WJ6Q6k9CM0DT482m2/FShhZPK6nrCKWSlsn5WZQ1tlLd3Bb/HNDDIwBHJVZP1RQNyGBXzZG4P1ndFjeVmlraeWDVp/xs5kQCmva9sjtuOP+0gUHTnxC6ERKajtAMhG6geZ1FeQ2krqF5DccYXXPUYy2HdJrUWcPzufbFldR3dHY1uSq5ZUns9zy6eTfL91TE9wOGTns4EleYGXZUF0+lmFLuW7mO22ZMIKhpAAJ4suyOG4KnHEz4o5cXCU2fi6YjPF6EpiMML0I3kqQuDQeOco+5kGKg/B6DswoG8tCHm9jf3JrUsrjKS4QjhUCTIl6PdL+H/bWNWKYZT0ErlsJWJMrG8moeXL2en543kTRPUudxMHD/8cZ7XB5z183fFlddMK20KDerwLZMiEawo2HsSAQ73IEdDTvHYpW1EvwisVebmBLhjjCPfbwJDfj2+BHolh1PjcRrXDUlqvLzysO8s6OUsQP7k52WghKCyoZmdtYcobBfKv80thBh2T2bfyXDQsr8vHue+MqB53GBaduwfMHDT7/yTprfy40LZyEsEyzTgRKJdIGxTLAs7Ei4hzd0b2lco957+AjPfPolowdkcPHwPKSZfA04HqR5DZTPiCvKtCzKGlo43NqOadkMDAUYkhGCWDzd4brr3XVN9835z9d+eUrAmDv+/qYdjVyy4cudPLN0BVeeP41powsd1UQjDiQXkGU5++5x00yCFJd/orJMi01lh3hz6z4y/B4uHzmEDI+RNE/jDgN6M2roMacTXydCOdTSznNf7GqaVVTQ74on3jjmfM5XgjFLNxRgRfcTjUo7GsaKRnll+fus37KDy2dP4+xRQ+NA4utIuEs9CbCscKRLLVbMGxJVZVrUNLXy6sad1Le2Mzozg1lDcwl6dFaXHmJHzRHy01OYP3IwvtgAMhFIdzDxIJVkw8FaVuwu5/YLp+Lzeb6Vffv/WnpSYKJlm+8VVvRX2BbCVUA0zIGyCu599FkyUgKMHjKIy2ecRcCrJ6slEknej627A+pNQZZpsq2qnrV7ymkLRzivMJdJg7LYV9fIW9v2g20zPieTswuyCeg9JyLf2XmA3FCA/kE/L27cTV5GCt+eUozUFMrQ3s289cEFJwemYts6bGuqAyYKtgW2xdJ3V/OfL7zO6w8vZl9ZJX9btZajHZ3MmjCK2RNHI7C7fCdmzKUHq7jn2WXce808BqYF4oD66uQBmBGTX7y+houKC5k5LC+ukEg4wqaDtawrq+JoOOKGg4g1YO/vKSfFazB/5GCumjSKkN/rtIxOh/Go1LWU9O/f12c6HRNMpHqvhmW1YFteYdsOFCuKiMFJVBGWSbSzgzXrNrDms414NI2F0yYwYVgBmBHsaISa+gYeeH4Zd1x1IelePQlabwNKOzbD9/t3PmZ+cSHjcjKB3qdEe0uprikJmdQlkLqG0rVxKdcv3npCYMK15eOwrc1xEFYMhgspDqdLSUSjYJm0t7by1pqP2Lh9D/1DAb4991wGhALJTX03b0pMM8tMbuZ7A+Iet8yeUIBu8zVd4zqpawjdWBS4+o4lfcV+7G8JlDEZ28J2g1YkAXFBYRtdalLO2m94uPrShVy1MEz14TqeeWM5zS2tXL9gJkOyMhBaBDuqY0ciCE2P+4/UdIhGkLGxVmKz3de3C4reDddtwRLnbYRugJQIzZiMMw379cHYSi/Bjt3QVQp0geq+WI75JkISukFOno+f/+AG2lpa+K/XlnGoppbvzD2XkYOyEFoEK9LJ06s/o6zqML+48kJ0r+GkqGUiYkoC4rB6g9O9JM3sSeXAkAo0HSEVQjdKjhX7MVPpb6++ug6sqbm5uQzMziIzIx0ppaOUGKweqeV6kBl1OoLuMSsKVhTCnbS3tvLcmyvYc6CcyUVD+fOLb/LFrgNIKeifmsJrd9/C2IIcpyWLNfsuHNvq5pfdwcQHpjEgsWMxGM55qRBS1XrOv27ACYE5UF7xUdmB0ukHDx6kuqqK+vp6sG2ca2xCoRBjRo1k/PhxZPXvB2a0C1QMhLCiXZC6ATLDncy67ieEo1EunTmFK847i8+27Wbxk6+w9anfoblDpMRpjO5geitSxZQSh9AFTSraOiJsO1DZPuPmXwZOCMzcefPr/+PPj2Tk5uZ03dO9UAiamxrZsX07WzZvovbwYWzbIhqNUF5ewZJH/4omSQaSCMgMs3X7DiZefC2HPnyd595cwQeffsGYofn8YcmrLJxewpO/uoW0gK8LRmLvNnbs3qffYGD/DL538eyk4AFaO8Lsqqhi+4GDlFXXYdo2Qgj8Xi9rN2yxV36yoc9B9DE9pn9WdqfyeOmMdsFz+wlS2HiDIUqmTqNk6jSEcKAtf3sZmzY/yt33PcCg3ByuuOxbZKanYltRhBlx1lYU29TpsHUAmsI2RUUjSEkJseiy+fxhyav4g0Gefu8fNDY734QIbBSCtFCAFL8Pr2EgBFQcaeVwawf3v7yCsKsqIbGBFL+foiF5zJg2hevyclCacz9bSDr/uqR3c3LjPJZiSuta1gCzkoAg4oCkcCY6pBA99pWAiooKlv7tNY4cqWfG9GnMPX8WyjaTVPTj2++ivPIgj91/NwGfh8X/8Rc+3biFtc//7645kZjXRKMmjU3NtLS10xEOY9tg6BqhlCBpoSAeT+wrrZhihFLYQoCQzhKDFivNavDE1BMCs6O6eZmNvVAgnKDjcJxUAuLHZazXqUQXKHdbYvOPjz9i9fvvkRoKcfUV3yI/JxthmYQ7O7jj7t/wf55/mXAkzEXnz+SR397JgH7pMb9KaPWgax2PQPa6tl0YiVCSS5U2aFRObye+Esy6/TUP1NXV/nTAgGx0TUNJgSBRLV1wlHBguaCUuw0o6ShISUFjfT1/e+0Vqg4dIn9QLhcvmEtu1gAwo1jRsHMPt8PYvUPpFne7BwzRdUw6QOzeoQC8bwwYPKevk8f0mFUrVpTX1x3m7FlzWfbCU4wcO57sgbkIoGhMMT6vNz7D5sCyEQiUBIljdEqCtEBJEKZNMD2DRTf9ACHgUGUlr7+9jOqqKtJSQ8y7YDbFo0YibLOrZbMt7BgM0V0t0BV493QR0gHl7idcG+uPbThW7MdUzOtfVg0zbXsPOArQpaCx7jD7t29m9LiJLHvxadLS0zl3znzsaJghhcNRUiaAcq5LVJCKwVIJ6aakoLmxkTXvv8fO7dsAm8H5eUw9q4SiYcNQUvRMoaQouqC0trdTfvAQ5eUVVFQepK6+HtM0SUtNZfjwYRSNGM7A7Gw8una1JyX95RMCs+TzcmHZNJi2nQqgS4GuJLoUSNuisa6a7Jw82prq+eLDDygoHM7urV9gWxZXXrsIXSkMw4iDqijdz4O//Xfuuvd+BuXlxYG5cBI9qby8nA3rP2PP7l289dZbjBo1ihHDh5FYXyEEto1zTAhs28bvD5CXn09KSpChw0aQn5+PUorGhiPs27uX3bt2UVNTzeCCgpIbFi364oTAADywdu9q07Jng1N5Q5PoUrJ++ausfnkJ9zz9Jh7Dg6ZEXBVHmxuxImFWvPIsoyZMwmt4yB44kP6ZmTz/+F+46Ue3kRIKxdUjccE4+64vSQHYNg/96Y9cdPFCikeP6VE/V0duGJZtYwM33XAduXn5/Ps99ya1lg5QGtOD/vRjxf2VYL7/X6tul7rn9wc2rSOrcCTZBcPwGQoR6aC+fA/Dx5WgK4GMpZqSAl3KOChdCpob6tj88VomTD2Hd195luGjipk0ZRqpaSH8Pn9cUd1BdW/tSAguDsYdv8X3nfWunTtJTU0jKzu7t67Fs1mpgWtPCszs2/40IGvM1NLmmoN+JQVtNWU0lu1i6mXX4Q8ECQT8eDSJEVt0KdFdKAnAdCXjHhONdFJ1YB+frlnJnEsu46OVbzNy9DjOnX1+nx7ldhGk6FlHKyEENx5XSYnXuUyVEOfkZQT/cVJgAOb95eMnTdu+0XmiAq8mkFaYfateICMnn+El55DaLxOPrnpAUhJ06UDRlYynG4CUDjQ7GuVwZSlmJMInq95m2qw5dLa3MTA3l2EjRmJoKqmb0FdJBhQLMKFrEYOzacSA0MSvivm4wMx4YM0k27I3WJaN0iRKSTRDYSiJz1C0lG6hZstHTPof1+IzNNIzszA058m7kBwgsVQRXX7keImzn5h+dVWV7N78OeMmn83yl59h5NgJDBk2HE1JcvIK8BhGUh3NPuLoum+8D/b94oGpj50SMACT7nr3U2AK4IDRHTiarjB0hc9QaFYn5R8uJT0rh8y8IfTLzSMYTEFJgUeTjsHGHrm77VZcYKEBuq7FfcpVmRSgSUHVgX3s2bqJknPOY/mrz+HzB5g8YzZr31lKcclUmhsbKNu7i4v/+Xrefulp0vr1Z/TEyXyy6h3mXnI5BYWFTT6PJ7ckL73tlIHJmfuT76hA2jNWZ7sQwiKtaDpGwO8A0h1AmqHw6oqgV6P10F42Pf8QU665hYay3QyZcDaZ+UNRUvaAo0nB+0sepqWumu/e+UAPf3JVpUuBlIJoZwevP/kIC665kfR+/fqssxLO54VtEensYP0H7z30m/95063HE+9x/wD60MqHnxv8Ly+eK237B9HmKlrrG2lZ+ywpQyaQNnxyDIwk4tE42hlF+jLB46eptYO0MdOpa2ikqnIttbs2UTTzYsq/+DuB9P4UTpkFZpT8qXPoaGqgvjWcpDA3DV1QSgia6o6we9tWig8eRAW7xoGJLZZKGMtJKdB177qpcy/5t+ON92v9Pib7ykeCrduXl3tzx6V7MgtRmgGWSbh6C3aklczJCzF8XgyPiqtIahJdxXzGTR8pELZJuLkeIRUV698jI6+QaEsDWFHGzFqIx+vF0FSPNExKx3jwCUDcoVIMYmtDHSuffyJ61qx5Z/36hks3nRYwACrQ7x1/0dz5npzxQmoGyuNFM3zY4VaUJumo/ILMkovwBoNohvO1hWvYQgpnW4he/cbQJG2HK/AHg+xc8SIDhxczaMwkAqFUvB5Pj88D8V9C9HbOUJLq0j0s+fVPth7ct3OubdvVpw0MQMrMn/9ZSPUjoRRKM1CGD+XxoXsDWB3NKBHFaqsmbdR0DJ8XpSRKE86gUnMACTf/Y4uUAkMlpI8UaNLmyL4vqdm+nlGzL2X3B2+Q0m8AQ0pm0NZQS/9BQ/AEUmIwZBIY17sMTX6opJh924zCr/XuwQmB8U29RQEvCamukJqB1Aw0jw9l+NB8AXRvEKv9CJG6XfhzRmAEUvCkD+ymHAeOC0VI0auqeqSgZRJub6ahdDuhATnU7dtOW90hxlx4BbvWvEFe8WQKxk2JXau2KClm3jNvZOPXjfGEf85qTLzREFIuFVLNF1IhY8rRPD40bzAOyI520FG5Af+gYtr2fUIwv5iUgrE9YNhmGGFH0QMpjqp6UVRiCvaWisIyMdsbaTywk5bqAzXFF1w24bEbZh53+pwSMAD66H8y7Naa1SI1f7o0fMTV4wuie4OxFPOiYscBrNZqBCZtB9aTWjQdPZCGkdqfmr8vIdpaz9DLfxGDQRxaHNBxwIopbJPV3jj/pR9eeMJvppz0T+a1YQsUsAQr+h0RyBTS8DtAYr6jxbaFUkhNj0MSUiGURvv+jzHbavHnjCTSXE2/cXOQmt5DUUI6zW739Ev0Ld3xqHcNTf7zGzed3XQycZ2yt0/0sd+Zax3Z84LMGJ6BMlC6EQckdSMORGpGEiQ3DbEtwod3oHxBwnWlKMNL6uhZaLoWV4rUpNNL7gZKaRKpZLOU4rYPbpv55KmI55S+lmNMvDFodzT+xarbeZ3MnoA0/HHvcYEkKkZqhvOzV6l6KMlsqUZqOi07VhHIK8afOwrdH0hKpwQjf1cqefMnd1xQ8dW1PL5yWt5w08ddO4do+x/sjqaJMjXfSY0kGEYcRvIx6WzHjjnndISwaT/wKeHa/fSbeiWaJ6ZIJfcKKX638bcLnjrVMZy2V/8AtGELzhOa5xZb815i1+/2a1nFKF9qHIaQCql3B5GsoniqIWje+gb+/Elm56EtW4NDSu4JDCx848s/XnJaAjitYOI3Sc0by9HG11TBjAYVyhkhNSOtO5zuIJzjuqugo5iRrTUr7ktVvtCdRys3v3ra6/xN/zcQY+KNQkg5VEhVIjWjREg1WmpGQEjlk7ohpVRHpWYcFVLtk5qxQSj1udT0HYde+H70m6znNw7m/5dy5k39PsoZMH2UM2D6KGfA9FHOgOmjnAHTR/lvGJ5lYZz+CxsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP002/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 704799  =      0.000 ...   704.799 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 19.9s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 2 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 156 iterations.\n",
            "GEV of found microstates: 0.619727\n",
            "Converged at 48 iterations.\n",
            "GEV of found microstates: 0.618642\n",
            "Converged at 80 iterations.\n",
            "GEV of found microstates: 0.620732\n",
            "Converged at 47 iterations.\n",
            "GEV of found microstates: 0.617844\n",
            "Converged at 44 iterations.\n",
            "GEV of found microstates: 0.618340\n",
            "Converged at 124 iterations.\n",
            "GEV of found microstates: 0.620300\n",
            "Converged at 55 iterations.\n",
            "GEV of found microstates: 0.620371\n",
            "Converged at 56 iterations.\n",
            "GEV of found microstates: 0.617664\n",
            "Converged at 53 iterations.\n",
            "GEV of found microstates: 0.620081\n",
            "Converged at 79 iterations.\n",
            "GEV of found microstates: 0.619404\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGIAAAD8CAYAAACMyXE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAHZ0lEQVR4nO3dXYicZxnG8f/Vxg+ItYUmgthqBNPGIELroIUeGGiFtAf1QBEDxQ9C90RFsAiKUkWPakFBqB8rhmjBaO2BLLjSA40UpCmd0FraSCXUr6iQNA05KVYLtwfvRNbtzs673Sc7V2auHwTm42HmCf/MvDMD7x1VFTF9l017A9FJCBMJYSIhTCSEiYQwMTGEpEOSTkt6esz9kvRtSSclPSXpxvbbnH19XhGHgf3r3H8bsHv0ZwH47ua3NX8mhqiqR4AX1lnyQeDH1TkGXCXpza02OC+2NXiMtwB/W3H91Oi2f65eKGmB7lXD9u3b37Nnz54GT+/t+PHjz1fVzknrWoToraoWgUWAwWBQw+FwK59+KiT9pc+6Fp+a/g5cu+L6NaPbYgNahFgCPjb69HQTcL6qXvG2FOub+NYk6QiwD9gh6RTwFeA1AFX1PWAZuB04CbwIfPJibXaWTQxRVQcm3F/Ap5rtaE7lm7WJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECZ6hZC0X9Kzo3kbX1jj/rdKOirpidE8jtvbb3W29RmKcjlwP93Mjb3AAUl7Vy37MvBgVd0AfBT4TuuNzro+r4j3Aier6rmq+jfwU7r5GysV8MbR5SuBf7Tb4nzoE2LcrI2VvgrcOToPexn4zFoPJGlB0lDS8MyZM69iu7Or1cH6AHC4qq6hO/n9AUmveOyqWqyqQVUNdu6cOCdkrvQJ0WfWxkHgQYCqehR4PbCjxQbnRZ8QjwO7Jb1d0mvpDsZLq9b8FbgFQNI76ULkvWcD+gzOehn4NPAw8Ae6T0fPSPqapDtGy+4G7pL0e+AI8InKiOUN6TWvqaqW6Q7CK2+7Z8XlE8DNbbc2X/LN2kRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmEgIEwlhIiFMJISJhDCRECYSwkRCmGgyAmK05iOSTkh6RtJP2m5z9vX5j8UvjID4AN3J7o9LWhqdN3dhzW7gi8DNVXVO0psu1oZnVasREHcB91fVOYCqOt12m7Ov1QiI64DrJP1O0jFJ+9d6oIyAGK/VwXobsBvYRzcO4geSrlq9KCMgxms1AuIUsFRV/6mqPwF/pAsTPbUaAfELulcDknbQvVU913CfM6/VCIiHgbOSTgBHgc9X1dmLtelZpGmNzBgMBjUcDqfy3FtJ0vGqGkxal2/WJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYaDaLY7TuQ5JK0sRzxuL/TQyxYhbHbcBe4ICkvWusuwL4LPBY603Og1azOAC+DtwL/Kvh/uZGk1kckm4Erq2qX673QJnFMd6mD9aSLgO+Cdw9aW1mcYzXYhbHFcC7gN9K+jNwE7CUA/bGbHoWR1Wdr6odVbWrqnYBx4A7qmr2xwo01GoWR2zSxFFyAFW1DCyvuu2eMWv3bX5b8yffrE0khImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhImEMJEQJhLCREKYSAgTCWEiIUwkhIkmIyAkfU7SCUlPSfq1pLe13+psazUC4glgUFXvBh4CvtF6o7OuyQiIqjpaVS+Orh6jOxc7NqDJCIhVDgK/WuuOjIAYr+nBWtKdwAC4b637MwJivD7nWU8aAQGApFuBLwHvr6qX2mxvfmx6BASApBuA79ONfjjdfpuzr9UIiPuANwA/l/SkpKUxDxdjNBkBUVW3Nt7X3Mk3axMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTCSEiYQwkRAmEsJEQphICBMJYSIhTLSaxfE6ST8b3f+YpF2tNzrrWs3iOAicq6p3AN8C7m290VnXZBbH6PqPRpcfAm6RpHbbnH19Tu9daxbH+8atqaqXJZ0HrgaeX7lI0gKwMLr6kqSnX82mLzHX91nU6zzrVqpqEVgEkDSsqsFWPv80SBr2WdfnranPLI7/rZG0DbgSONtnA9FpMotjdP3jo8sfBn5TVdVum7Nv4lvT6D3/wiyOy4FDF2ZxAMOqWgJ+CDwg6STwAl2sSRY3se9LSa+/p/IP10O+WZtICBNTCTHpJ5NZIOmQpNN9vytteYieP5nMgsPA/r6Lp/GK6POTySWvqh6h+wTZyzRCbHR86VzIwdrENEL0Gl86b6YRos9PJnNny0OMG1+61fu42CQdAR4Frpd0StLBddfnJw4POVibSAgTCWEiIUwkhImEMJEQJv4L9xQ1uKlxrVcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP002/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 704799  =      0.000 ...   704.799 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 12 components\n",
            "Fitting ICA took 13.2s.\n",
            "Using EOG channels: HEOG, VEOG\n",
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering ICA sources\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "... filtering target\n",
            "Setting up band-pass filter from 1 - 10 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:\n",
            "- Windowed frequency-domain design (firwin2) method\n",
            "- Hann window\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 0.75 Hz)\n",
            "- Upper passband edge: 10.00 Hz\n",
            "- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 10.25 Hz)\n",
            "- Filter length: 10000 samples (10.000 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.7s finished\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying ICA to Raw instance\n",
            "    Transforming to ICA space (12 components)\n",
            "    Zeroing out 2 ICA components\n",
            "    Projecting back using 28 PCA components\n",
            "Finding 5 microstates, using 10 random intitializations\n",
            "Converged at 94 iterations.\n",
            "GEV of found microstates: 0.618551\n",
            "Converged at 37 iterations.\n",
            "GEV of found microstates: 0.618875\n",
            "Converged at 67 iterations.\n",
            "GEV of found microstates: 0.623221\n",
            "Converged at 53 iterations.\n",
            "GEV of found microstates: 0.622759\n",
            "Converged at 41 iterations.\n",
            "GEV of found microstates: 0.623577\n",
            "Converged at 76 iterations.\n",
            "GEV of found microstates: 0.618796\n",
            "Converged at 75 iterations.\n",
            "GEV of found microstates: 0.618880\n",
            "Converged at 140 iterations.\n",
            "GEV of found microstates: 0.618474\n",
            "Converged at 57 iterations.\n",
            "GEV of found microstates: 0.616772\n",
            "Converged at 55 iterations.\n",
            "GEV of found microstates: 0.620352\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting parameters from /content/drive/My Drive/drdo/subjects/VP001/nback1.vhdr...\n",
            "Setting channel info structure...\n",
            "Reading 0 ... 701439  =      0.000 ...   701.439 secs...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:3: RuntimeWarning: Channels contain different lowpass filters. Highest (weakest) filter setting (1000.00 Hz) will be stored.\n",
            "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EEG channel type selected for re-referencing\n",
            "Applying average reference.\n",
            "Applying a custom ('EEG',) reference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-d382f9848599>:4: RuntimeWarning: Fiducial point nasion not found, assuming identity unknown to head transformation\n",
            "  raw.set_montage(TUB_montage)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 1 - 40 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 1.00\n",
            "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
            "- Upper passband edge: 40.00 Hz\n",
            "- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)\n",
            "- Filter length: 3301 samples (3.301 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting ICA to data using 28 channels (please be patient, this may take a while)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.9s finished\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting by explained variance: 8 components\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-9d74f2004a01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mTUB_montage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_custom_montage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclean_folder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-20-018d1d34c7ae>\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(subjectPath)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfolder_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdirs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m           \u001b[0mpath_file_fif1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubjectPath\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfolder_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/nback1.vhdr'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m           \u001b[0mmicrostate_analysis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_file_fif1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolder_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'nback1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m           \u001b[0mpath_file_fif2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubjectPath\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfolder_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/nback2.vhdr'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-24-d382f9848599>\u001b[0m in \u001b[0;36mmicrostate_analysis\u001b[0;34m(file_path, subject_name, sesssion_name)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0;31m# Clean EOG with ICA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m   \u001b[0mica\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mICA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m   \u001b[0mbads_eog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_bads_eog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m   \u001b[0mica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexclude\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbads_eog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-565>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inst, picks, start, stop, decim, reject, flat, tstep, reject_by_annotation, verbose)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/mne/preprocessing/ica.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, inst, picks, start, stop, decim, reject, flat, tstep, reject_by_annotation, verbose)\u001b[0m\n\u001b[1;32m    657\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseRaw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m             self._fit_raw(inst, picks, start, stop, decim, reject, flat,\n\u001b[0m\u001b[1;32m    660\u001b[0m                           tstep, reject_by_annotation, verbose)\n\u001b[1;32m    661\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/mne/preprocessing/ica.py\u001b[0m in \u001b[0;36m_fit_raw\u001b[0;34m(self, raw, picks, start, stop, decim, reject, flat, tstep, reject_by_annotation, verbose)\u001b[0m\n\u001b[1;32m    702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_samples_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'raw'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/mne/preprocessing/ica.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, data, fit_type)\u001b[0m\n\u001b[1;32m    841\u001b[0m             ica = FastICA(\n\u001b[1;32m    842\u001b[0m                 whiten=False, random_state=random_state, **self.fit_params)\n\u001b[0;32m--> 843\u001b[0;31m             \u001b[0mica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    844\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munmixing_matrix_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mica\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_fastica.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mitself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \"\"\"\n\u001b[0;32m--> 634\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_sources\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_fastica.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, compute_sources)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"parallel\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ica_par\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"deflation\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m             \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ica_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_fastica.py\u001b[0m in \u001b[0;36m_ica_par\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    105\u001b[0m     \u001b[0mp_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m         \u001b[0mgwtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg_wtx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m         \u001b[0mW1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sym_decorrelation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgwtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mp_\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mg_wtx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mgwtx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg_wtx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/decomposition/_fastica.py\u001b[0m in \u001b[0;36m_logcosh\u001b[0;34m(x, fun_args)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mgx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# apply the tanh inplace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mg_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;31m# XXX compute in chunks to avoid extra allocation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f80f69fee50> (for post_execute):\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    107\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m                     \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mf_mgr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m                 \u001b[0mf_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0matexit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroy_all\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1945\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1946\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1949\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    391\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    392\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 393\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2628\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2629\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2630\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2632\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1231\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mticks_to_draw\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1232\u001b[0;31m             \u001b[0mtick\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1234\u001b[0m         \u001b[0;31m# scale up the axis label box to also find the neighbors, not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    295\u001b[0m         for artist in [self.gridline, self.tick1line, self.tick2line,\n\u001b[1;32m    296\u001b[0m                        self.label1, self.label2]:\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    866\u001b[0m                     \u001b[0mmarker_trans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker_trans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    867\u001b[0m                 renderer.draw_markers(gc, marker_path, marker_trans,\n\u001b[0;32m--> 868\u001b[0;31m                                       \u001b[0msubsampled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maffine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    869\u001b[0m                                       fc_rgba)\n\u001b[1;32m    870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mfrozen\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1764\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1765\u001b[0m         \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1766\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mAffine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1768\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mget_matrix\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1876\u001b[0m         \u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1877\u001b[0m         \"\"\"\n\u001b[0;32m-> 1878\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1879\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inverted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 215.568x144 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def read_data(subjectPath):\n",
        "    \n",
        "    try:\n",
        "        root, dirs, files = next(os.walk(clean_folder))\n",
        "\n",
        "        \n",
        "        for folder_name in dirs:\n",
        "          path_file_fif1 = subjectPath + '/' + folder_name + '/nback1.vhdr'\n",
        "          microstate_analysis(path_file_fif1, folder_name,'nback1')\n",
        "\n",
        "          path_file_fif2 = subjectPath + '/' + folder_name + '/nback2.vhdr'\n",
        "          microstate_analysis(path_file_fif1, folder_name,'nback2')\n",
        "\n",
        "          path_file_fif3 = subjectPath + '/' + folder_name + '/nback3.vhdr'\n",
        "          microstate_analysis(path_file_fif1, folder_name,'nback3')\n",
        "            \n",
        "    except StopIteration:\n",
        "        pass\n",
        "        print(\"Error ocurred:\")\n",
        "        print(\"Directory with dataset does not found!\")\n",
        "        print(\"Program will be terminated\")\n",
        "        exit(1)"
      ],
      "metadata": {
        "id": "MVKVK-PLkfpZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def microstate_analysis(file_path, subject_name, sesssion_name):\n",
        "\n",
        "  raw = mne.io.read_raw_brainvision(file_path, eog=('HEOG', 'VEOG'), preload=True)  \n",
        "  raw.set_montage(TUB_montage)\n",
        "\n",
        "  raw.pick_types(meg='mag', eeg=True, eog=True, ecg=True)\n",
        "\n",
        "  # Microstate analysis needs average reference\n",
        "  raw.set_eeg_reference('average')\n",
        "\n",
        "  # Filter the data. Make sure slow drifts are eliminated.\n",
        "  raw.filter(1, 40)\n",
        "\n",
        "  # Clean EOG with ICA\n",
        "  ica = mne.preprocessing.ICA(0.99).fit(raw)\n",
        "  bads_eog, _ = ica.find_bads_eog(raw)\n",
        "  ica.exclude = bads_eog[:2]\n",
        "  raw = ica.apply(raw)\n",
        "\n",
        "  # Select sensor type\n",
        "  raw.pick_types(meg=False, eeg=True)\n",
        "  # raw.pick_types(meg='mag', eeg=False)\n",
        "\n",
        "  # Segment the data into 5 microstates\n",
        "  maps, segmentation, polarity = mne_microstates.segment(raw.get_data(), n_states=5,\n",
        "                                                         random_state=0,\n",
        "                                                         return_polarity=True)\n",
        "    \n",
        "  # Plot the topographic maps of the microstates and part of the segmentation\n",
        "  mne_microstates.plot_maps(maps, raw.info)\n",
        "  mne_microstates.plot_segmentation(segmentation[:500], raw.get_data()[:, :500],\n",
        "                                    raw.times[:500], polarity=polarity[:500])\n"
      ],
      "metadata": {
        "id": "RJ1vYS-Qm1ye"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}