EEG_RestingState_Pipeline.py

###############################################################################################
# Emma Bailey, 18/10/2022
# Wrapper script for project to investigate high frequency oscillations in the human brain
###############################################################################################
# Can run a bad channel check for data quality purposes, but no channels are excluded before running CCA

import numpy as np
from Common_Functions.import_data_rs import import_data
from Common_Functions.Create_Frequency_Bands_RS import create_frequency_bands_rs

if __name__ == '__main__':
    srmr_nr = 2  # Set the experiment number

    if srmr_nr == 1:
        n_subjects = 36  # Number of subjects
        subjects = np.arange(1, 37)  # (1, 37) # 1 through 36 to access subject data
        conditions = [1]  # Conditions of interest
        sampling_rate = 5000  # Frequency to downsample to from original of 10kHz

    elif srmr_nr == 2:
        n_subjects = 24  # Number of subjects
        subjects = np.arange(1, 25)  # (1, 25) # 1 through 24 to access subject data
        conditions = [1]  # Conditions of interest - tib digits and med digits, also including mixed nerve now
        sampling_rate = 5000  # Frequency to downsample to from original of 10kHz

    ######## 1. Import ############
    import_d = True  # Prep work

    ######## 2. Freq band ##########
    split_bands_flag = True

    ############################################
    # Import Data from BIDS directory
    # Select channels to analyse
    # Remove stimulus artefact by PCHIP interpolation
    # Downsample and concatenate blocks of the same conditions
    # Also notch filters powerline noise and hpf at 1Hz
    ############################################
    if import_d:
        for subject in subjects:
            for condition in conditions:
                import_data(subject, condition, srmr_nr, sampling_rate, esg_flag=False)

    ###################################################
    # Split into frequency bands of interest
    ###################################################
    if split_bands_flag:
        for subject in subjects:
            for condition in conditions:
                create_frequency_bands_rs(subject, condition, srmr_nr, sampling_rate, channel_type='eeg')