def GC_cat_mat(sub='DiAs', cat='Face', multitrial=True, proc = 'preproc', ext = '.mat'): if multitrial == True: suffix = 'GC_multi_HFB_visual_epoch_' + cat else: suffix = 'GC_sliding_HFB_visual_epoch_' + cat subject = cf_load.Subject(sub) GC_fpath = subject.fpath(proc = proc, suffix=suffix, ext=ext) GC_mat = io.loadmat(GC_fpath) return GC_mat
preload = True tmin_pr = -0.5 tmax_pr = -0.1 tmin_po=0.1 tmax_po=0.5 preproc = 'preproc' sub_id = ['AnRa', 'AnRi', 'ArLa', 'BeFe', 'DiAs', 'FaWa', 'JuRo', 'NeLa', 'SoGi'] functional_group = {'subject_id': [], 'chan_name':[], 'category': [], 'brodman': [], 'DK': []} for sub in sub_id: #%% Import data subject = cf_load.Subject(sub) fpath = subject.fpath(suffix='lnrmv', proc = preproc) raw = subject.import_data(fpath) dfelec = subject.dfelec() # %% Extract HFB bands = HFB_process.freq_bands() # Select Bands of interests HFB_db = HFB_process.extract_HFB_db(raw, bands) # place and face id events, event_id = mne.events_from_annotations(raw) face_id = HFB_process.extract_stim_id(event_id) place_id = HFB_process.extract_stim_id(event_id, cat='Place') # Check test looks ok
def cp_elecinfo(subid, subject): subject = cf_load.Subject(subject) elecfile_src = elec_src(subid) elecfile_dest = subject.elecfile() copy(elecfile_src, elecfile_dest)
#HFB_tot_face = np.empty(shape =( nepochs, 0, nobs)) #HFB_tot_place = np.empty(shape =( nepochs, 0, nobs)) HFB_tot_pref = np.empty(shape=(nepochs, 0, nobs)) # need to define time before HFB_tot_npref = np.empty(shape=(nepochs, 0, nobs)) sub_id = [ 'AnRa', 'AnRi', 'ArLa', 'BeFe', 'DiAs', 'FaWa', 'JuRo', 'NeLa', 'SoGi' ] path_visual = cf_load.visual_path() df_visual = pd.read_csv(path_visual) for sub in sub_id: subject = cf_load.Subject(name=sub, task=task, run=run) fpath = subject.fpath(proc=preproc, suffix='lnrmv') raw = subject.import_data(fpath) face_chan = list(df_visual['chan_name'].loc[ df_visual['subject_id'] == sub].loc[df_visual['category'] == 'Face']) place_chan = list(df_visual['chan_name'].loc[ df_visual['subject_id'] == sub].loc[df_visual['category'] == 'Place']) bands = HFB_process.freq_bands() # Select Bands of interests HFB_db = HFB_process.extract_HFB_db(raw, bands) HFB_db = HFB_db.drop_channels(ch_names='TRIG') time = HFB_db.times # place and face id events, event_id = mne.events_from_annotations(raw) face_id = HFB_process.extract_stim_id(event_id)
@author: guime """ import HFB_process import cf_load import os import scipy as sp import re import numpy as np import mne import matplotlib.pyplot as plt import pandas as pd from pathlib import Path, PurePath #%matplotlib sub = 'KiAl' subject = cf_load.Subject(name=sub) subject_path = subject.subject_path() data_path = 'EEGLAB_datasets/raw_signal/KiAl_freerecall_1_preprocessed.set' fpath = subject_path.joinpath(data_path) fpath = os.fspath(fpath) raw = mne.io.read_raw_eeglab(fpath, preload=True) #raw.copy().pick('TRIG').plot(duration=50, scalings=1e-5) #%% raw_c = raw.copy() raw_c.pick('TRIG').plot(duration=250, scalings=1e-4)