quickeeg 0.0.14

This package provides a quick and easy pipeline to process EEG files and generate a report. It is a wrapper for mne.

Quick EEG

Quick EEG is an open-source python package to make processing EEG simpler. It is a wrapper for MNE that streamlines processing pipelines.

Official documentaton and official release of package on pip to come.

Example Code

Example use case, which can be found in quickeeg/quickeeg_main.py

import os
import sys
sys.dont_write_bytecode = True
import numpy as np

from helpers.preprocessing import Preprocessing
from helpers.report import Report

if __name__ == '__main__':

    ###########################################
    ############## Example usage ##############
    ###########################################

    #Subject information
    path = os.path.join('quickeeg','data')
    id = 'participant_001'

    #Create pipeline
    pipeline = ['load_data',
                'rereference',
                'filter',
                'notch_filter',
                'ica',
                'marker_cleaning',
                'epoching',
                'baseline_correction',
                'averaging']

    #Processing parameters
    target_markers = {'11': [f'{i}' for i in range(11, 20)],
                      '21': [f'{i}' for i in range(21, 30)],
                      '31': [f'{i}' for i in range(31, 40)]}
        
    params = {'pipeline':               pipeline,
              'file_path':              os.path.join(path, id),
              'find_files_by_marker':   's11',
              'reference_channels':     'average',
              'bp_filter_cutoffs':      [0.1, 50],
              'notch_filter_freq':      60,
              'ica_components':         20,
              'eog_channel':            ['1L', '1R'],
              'target_markers':         target_markers,
              'epoching_times':         [-.2, .8],
              'baseline_times':         [-.2, 0]}

    #Run the pipeline
    preprocessing = Preprocessing(**params)
    preprocessing.process()

    electrodes=list(np.arange(0,len(preprocessing.raw.ch_names)))
    preprocessing.plot_erp(electrode_index=electrodes, save_plot=True)

    #Build the report
    reader_note = ' '.join(['This report was produced by the QuickEEG package.'])
    
    custom_text = ['## Note for the reader', 
                    reader_note]
    
    report = Report(preprocessing)
    report.build_report(custom_text)