diff --git a/notebooks/32_Ripple_Detection.ipynb b/notebooks/32_Ripple_Detection.ipynb
index b9a37e279..bdd5c56a5 100644
--- a/notebooks/32_Ripple_Detection.ipynb
+++ b/notebooks/32_Ripple_Detection.ipynb
@@ -239,15 +239,29 @@
     "    pd.DataFrame(\n",
     "        (\n",
     "            sgc.Electrode\n",
-    "            & {\n",
-    "                \"nwb_file_name\": nwb_file_name,\n",
-    "            }\n",
+    "            & {\"nwb_file_name\": nwb_file_name, \"bad_channel\": \"False\"}\n",
     "        )\n",
     "        * (sgc.BrainRegion & {\"region_name\": \"hippocampus\"})\n",
     "    )\n",
-    "    .loc[:, [\"nwb_file_name\", \"electrode_id\", \"region_name\"]]\n",
+    "    .loc[\n",
+    "        :,\n",
+    "        [\n",
+    "            \"nwb_file_name\",\n",
+    "            \"electrode_id\",\n",
+    "            \"region_name\",\n",
+    "            \"electrode_group_name\",\n",
+    "        ],\n",
+    "    ]\n",
     "    .sort_values(by=\"electrode_id\")\n",
     ")\n",
+    "# for the purpose of the demo, we will only use one electrode per electrode group\n",
+    "electrodes_df = pd.DataFrame(\n",
+    "    [\n",
+    "        electrodes_df[electrodes_df.electrode_group_name == str(i)].iloc[0]\n",
+    "        for i in np.unique(electrodes_df.electrode_group_name.values)\n",
+    "    ]\n",
+    ")\n",
+    "\n",
     "# create lfp_electrode_group\n",
     "lfp_eg_key = {\n",
     "    \"nwb_file_name\": nwb_file_name,\n",
@@ -3038,7 +3052,25 @@
     }
    ],
    "source": [
-    "sgr.RippleParameters().insert_default"
+    "sgr.RippleParameters().insert_default()\n",
+    "sgr.RippleParameters.insert1(\n",
+    "    {\n",
+    "        \"ripple_param_name\": \"default_trodes\",\n",
+    "        \"ripple_param_dict\": {\n",
+    "            \"speed_name\": \"speed\",  # name of the speed field in the position data\n",
+    "            \"ripple_detection_algorithm\": \"Kay_ripple_detector\",\n",
+    "            \"ripple_detection_params\": {\n",
+    "                \"speed_threshold\": 4.0,\n",
+    "                \"minimum_duration\": 0.015,\n",
+    "                \"zscore_threshold\": 2.0,\n",
+    "                \"smoothing_sigma\": 0.004,\n",
+    "                \"close_ripple_threshold\": 0.0,\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")\n",
+    "sgr.RippleParameters()"
    ]
   },
   {
@@ -3220,6 +3252,25 @@
     }
    ],
    "source": [
+    "# insert the position parameter set\n",
+    "sgp.TrodesPosParams().insert1(\n",
+    "    {\n",
+    "        \"trodes_pos_params_name\": \"single_led\",\n",
+    "        \"params\": {\n",
+    "            \"max_separation\": 10000.0,\n",
+    "            \"max_speed\": 300.0,\n",
+    "            \"position_smoothing_duration\": 0.125,\n",
+    "            \"speed_smoothing_std_dev\": 0.1,\n",
+    "            \"orient_smoothing_std_dev\": 0.001,\n",
+    "            \"led1_is_front\": 1,\n",
+    "            \"is_upsampled\": 0,\n",
+    "            \"upsampling_sampling_rate\": None,\n",
+    "            \"upsampling_interpolation_method\": \"linear\",\n",
+    "        },\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")\n",
+    "# populate the position if not done already\n",
     "pos_key = {\n",
     "    \"nwb_file_name\": nwb_file_name,\n",
     "    \"trodes_pos_params_name\": \"single_led\",\n",
@@ -3842,7 +3893,7 @@
    "source": [
     "## Up Next\n",
     "\n",
-    "Next, we'll [extract mark indicator](./31_Extract_Mark_Indicators.ipynb).\n"
+    "We will learn how to [extract spike waveform features](./40_Extracting_Clusterless_Waveform_Features.ipynb) to decode neural data.\n"
    ]
   }
  ],
diff --git a/notebooks/py_scripts/32_Ripple_Detection.py b/notebooks/py_scripts/32_Ripple_Detection.py
index 9bd360ec3..1a85028e4 100644
--- a/notebooks/py_scripts/32_Ripple_Detection.py
+++ b/notebooks/py_scripts/32_Ripple_Detection.py
@@ -80,15 +80,29 @@
     pd.DataFrame(
         (
             sgc.Electrode
-            & {
-                "nwb_file_name": nwb_file_name,
-            }
+            & {"nwb_file_name": nwb_file_name, "bad_channel": "False"}
         )
         * (sgc.BrainRegion & {"region_name": "hippocampus"})
     )
-    .loc[:, ["nwb_file_name", "electrode_id", "region_name"]]
+    .loc[
+        :,
+        [
+            "nwb_file_name",
+            "electrode_id",
+            "region_name",
+            "electrode_group_name",
+        ],
+    ]
     .sort_values(by="electrode_id")
 )
+# for the purpose of the demo, we will only use one electrode per electrode group
+electrodes_df = pd.DataFrame(
+    [
+        electrodes_df[electrodes_df.electrode_group_name == str(i)].iloc[0]
+        for i in np.unique(electrodes_df.electrode_group_name.values)
+    ]
+)
+
 # create lfp_electrode_group
 lfp_eg_key = {
     "nwb_file_name": nwb_file_name,
@@ -274,7 +288,25 @@
 # ## Setting Ripple Parameters
 #
 
-sgr.RippleParameters().insert_default
+sgr.RippleParameters().insert_default()
+sgr.RippleParameters.insert1(
+    {
+        "ripple_param_name": "default_trodes",
+        "ripple_param_dict": {
+            "speed_name": "speed",  # name of the speed field in the position data
+            "ripple_detection_algorithm": "Kay_ripple_detector",
+            "ripple_detection_params": {
+                "speed_threshold": 4.0,
+                "minimum_duration": 0.015,
+                "zscore_threshold": 2.0,
+                "smoothing_sigma": 0.004,
+                "close_ripple_threshold": 0.0,
+            },
+        },
+    },
+    skip_duplicates=True,
+)
+sgr.RippleParameters()
 
 # Here are the default ripple parameters:
 #
@@ -300,6 +332,25 @@
 # set and for a given interval. We can quickly populate this here
 #
 
+# insert the position parameter set
+sgp.TrodesPosParams().insert1(
+    {
+        "trodes_pos_params_name": "single_led",
+        "params": {
+            "max_separation": 10000.0,
+            "max_speed": 300.0,
+            "position_smoothing_duration": 0.125,
+            "speed_smoothing_std_dev": 0.1,
+            "orient_smoothing_std_dev": 0.001,
+            "led1_is_front": 1,
+            "is_upsampled": 0,
+            "upsampling_sampling_rate": None,
+            "upsampling_interpolation_method": "linear",
+        },
+    },
+    skip_duplicates=True,
+)
+# populate the position if not done already
 pos_key = {
     "nwb_file_name": nwb_file_name,
     "trodes_pos_params_name": "single_led",
@@ -366,5 +417,5 @@
 
 # ## Up Next
 #
-# Next, we'll [extract mark indicator](./31_Extract_Mark_Indicators.ipynb).
+# We will learn how to [extract spike waveform features](./40_Extracting_Clusterless_Waveform_Features.ipynb) to decode neural data.
 #