Automatic preprocessing pipeline for the TDBRAIN dataset, using pyprep and MNE packages:

With pyprep package according to PREP Bidgely-Shamlo et al. 2015:

1. Remove line-noise without committing to a filtering strategy (optional)
2. Robustly reference the signal relative to an estimate of the 'true' average reference
3. Detect and interpolate bad channels relative to this reference
   • Detection by:
     1. extreme amplitudes (deviation criterion)
     2. lack of correlation with any other channel (correlation criterion)
     3. lack of predictability by other channels (predictability/RANSAC criterion)
     4. unusual high frequency noise (noisiness criterion)

With MNE:

4. Repair EOG, ECG, and EMG artifacts with ICA (fitting ICA to high-pass filtered copy of eeg data, but applying to unfiltered eeg data)
5. Bandpass filter (1, 100)
6. epoch data (optional)

Extra: The pipeline saves a .pdf file in the same directory as the preprocessed data to see the effect of the different preprocessing steps.

preprocess_pipeline.py:

Main file for running the pipeline. Takes the sourcepath of the directory containing the 'derivatives' folder of the TDBRAIN dataset. e.g:

python preprocess_pipeline.py 'D:\Documents\TD-BRAIN\TDBRAIN-dataset-derivatives\'

Can adjust preprocessing parameters below in the file.

preprocessing.py:

Contains the class which actually performs the preprocessing steps.

plotting.py

Contains the function and subfunctions to plot different diagnostics at different steps of the preprocessing pipeline.