This repository contains modules to preprocess and analyze electro-physiological brain data (mainly EEG). It is meant for data handling collected with BrainVision products.
Before installation, be sure to create an environment for adequate functionality. You can use the environment.yml file to create your own Anaconda environment.
You'll need to clone the GitHub repository to your local machine. To do this, navigate to the repository on GitHub and click on the "Clone or download" button. You can then copy the HTTPS or SSH URL for the repository.
Next, open a terminal window on your local machine and use the following command to clone the repository:
git clone https://github.com/mxschlz/EEG_Tools2.git
The whole module serves as a convenience wrapper for the MNE-Python library.
Here, instead of writing extended scripts for basic preprocessing, you can simply set all the parameters available for preprocessing with MNE-Python in a single config.py file.
The core modules can be found in the core directory. Here, the EEGPipeline is the main module for preprocessing EEG data. The misc.py module contains utility functions such as for calculating SNR ratios or setting the logging level for information output.
With the Analyzer you can load up your preprocessed evoked data for further analysis with MNE-Python.
Be sure to include the setting and data files in the same root directory. A pipeline processing example can be found in examples/run_pipeline.py.
The EEGPipeline takes a root_dir as argument. Be sure to organize your data in the root directory in the following structure:
- root_dir: # parent directory
-
|- setting # child directory containing following files: -
|- mapping.json # electrode mapping -
|- ica_reference.fif # optional -
|- config.py # configuration for preprocessing -
|- montage.bvef # electrode scalp coordinates -
|- data # child directory containing subject directories -
|- sub_01 # child directory containing electrophysiological data -
|- sub_02 -
|- sub_03 -
... -
|- sub_n