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Part_ERP.py
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def Cat_all(sub):
aaa = 'no_ica' # no ica
xx = 1
suffics = { 1:'no_max',
2:'max_wo_head',
3:'max_w_head'}
for xx in [1,2,3]:
participant_arr=list(Part_info.keys())
participant=participant_arr[sub-1]
data_path ='/rds/projects/k/kowalcau-opm-recordings/cross_modal_project/'+participant
result_path=data_path+'/proccessed/'+suffics[xx]
old=1 if Part_info[participant]<109 else 0
data_name = 'full'
if aaa == 'no_ica':
ending = '_ann-1-wo-bad-chn.fif' if xx==1 else '_ann-1.fif'
else :
ending = 'ica-1.fif'
path_data = os.path.join(result_path,data_name +'_'+suffics[xx] + ending)
data_raw = mne.io.read_raw_fif(path_data, allow_maxshield=True,preload=True,verbose=True)
#events = mne.find_events(data_raw, stim_channel='STI101', min_duration=0.001001, mask_type='not_and', mask=(pow(2,8)+pow(2,9)+pow(2,10)+pow(2,11)+pow(2,12)+pow(2,13)))
if old==1:
result_path_eve=data_path+'/proccessed/'
filename_events = op.join(result_path_eve,data_name + '_eve-all-new' +'.fif')
events = mne.read_events(filename_events, verbose=True)
else:
events = mne.find_events(data_raw, stim_channel='STI101', min_duration=0.001001, mask_type='not_and', mask=(pow(2,8)+pow(2,9)+pow(2,10)+pow(2,11)+pow(2,12)+pow(2,13)),output='step')
filename_events = op.join(result_path,data_name + '_eve-all' +'.fif')
mne.write_events(filename_events, events,verbose=True,overwrite=True)
cat=np.array([240, 128, 64, 32, 192, 160, 96, 224])
onset_tr_wrd=cat+1
onset_tr_pic=cat+2
#check how many wrong/right answers
qst_left, qst_right, error_count = np.where(events[:,2]==5)[0], np.where(events[:,2]==6)[0], []
error_count += list(qst_left[events[qst_left+2,2]==8]-6)
error_count += list(qst_right[events[qst_right+2,2]==16]-6)
error_count += list(np.where(events[:,2]==24)[0])
wrong_answers = np.concatenate([qst_left[events[qst_left+2,2]==8]-6, qst_right[events[qst_right+2,2]==16]-6, np.where(events[:,2]==24)[0]])
all_qst = len(qst_left) + len(qst_right)
mask = np.logical_or(events[:,2] == 191, events[:,2] == 127)
ind = np.where(mask == True)
fig1=plt.hist(error_count, bins=ind[0], edgecolor='k')
plt.xlabel('runs')
plt.ylabel('errors per run (%)')
plt.title('Overall failed questions ' + str(round(len(error_count)/all_qst*100,2)) + '%')
filename_fig = op.join(result_path, 'Error_qst.png')
plt.savefig(filename_fig, dpi=600)
np.save(result_path+'Error_qst', error_count)
# exclude trials before failed questions
events[error_count,2]=-1
filename_events = op.join(result_path,data_name +'_'+suffics[xx]+ '_eve-right' +'.fif')
mne.write_events(filename_events, events,verbose=True,overwrite=True)
raw_list = list()
events_list = list()
path_file = os.path.join(result_path,data_name +'_'+suffics[xx] + ending)
raw = mne.io.read_raw_fif(path_file, allow_maxshield=True,verbose=True)
aa = 1
if aa==1:
if np.where(events[:,2]==24)[0].size==0:
events_id = {
'fix':255,
'break':1,
'qst_left':5,
'qst_right':6,
'ans_left':8,
'ans_right':16,
'start_000/w':240+1,
'start_100/w':32+1,
'start_010/w':64+1,
'start_110/w':96+1,
'start_001/w':128+1,
'start_101/w':160+1,
'start_011/w':192+1,
'start_111/w':224+1,
'start_000/p':240+2,
'start_100/p':32+2,
'start_010/p':64+2,
'start_110/p':96+2,
'start_001/p':128+2,
'start_101/p':160+2,
'start_011/p':192+2,
'start_111/p':224+2,
'words':191,'pictures':127 }
else:
events_id = {
'fix':255,
'break':1,
'qst_left':5,
'qst_right':6,
'ans_left':8,
'ans_right':16,
'start_000/w':240+1,
'start_100/w':32+1,
'start_010/w':64+1,
'start_110/w':96+1,
'start_001/w':128+1,
'start_101/w':160+1,
'start_011/w':192+1,
'start_111/w':224+1,
'start_000/p':240+2,
'start_100/p':32+2,
'start_010/p':64+2,
'start_110/p':96+2,
'start_001/p':128+2,
'start_101/p':160+2,
'start_011/p':192+2,
'start_111/p':224+2,
'words':191,'pictures':127}
raw_list.append(raw)
events_list.append(events)
raw, events = mne.concatenate_raws(raw_list,events_list=events_list)
del raw_list
epochs = mne.Epochs(raw,
events, events_id,
tmin=-0.40 , tmax=1,
baseline=None,
proj=True,
picks = 'all',
detrend = 1,
#reject=reject,
reject_by_annotation=True,
preload=True,
verbose=True)
fig=epochs.plot_drop_log()
filename_fig = op.join(result_path, 'drop-trials.png')
fig.savefig(filename_fig, dpi=600)
path_outfile = os.path.join(result_path,data_name+'_'+suffics[xx]+'_epo-right.fif')
epochs.save(path_outfile,overwrite=True)
pic=mne.event.match_event_names(event_names=events_id,keys=['p'])
word=mne.event.match_event_names(event_names=events_id,keys=['w'])
evoked_pic= epochs[pic].copy().average(method='mean').filter(0.1, 30).crop(-0.1,0.4)
evoked_word= epochs[word].copy().average(method='mean').filter(0.1, 30).crop(-0.1,0.4)
#evoked_word.copy().apply_baseline(baseline=(-0.1, 0))
#fig1=evoked_word.copy().pick_types(meg='mag').plot_topo(title = 'Magnetometers/WORDS')
#filename_fig = op.join(result_path, 'TOPO_mag_words.png')
#fig1.savefig(filename_fig, dpi=600)
evoked_pic.copy().apply_baseline(baseline=(-0.1, 0))
fig2=evoked_pic.copy().pick_types(meg='mag').plot_topo(title = 'Magnetometers/PICTURES')
filename_fig = op.join(result_path, 'TOPO_mag_pic'+aaa+'.png')
fig2.savefig(filename_fig, dpi=600)
#fig5=evoked_pic.plot()
#filename_fig = op.join(result_path, 'ERP_amp_pictures.png')
#fig5.savefig(filename_fig, dpi=600)
#evoked_word.copy().apply_baseline(baseline=(-0.1, 0))
#fig3=evoked_word.copy().pick_types(meg='grad').plot_topo(title = 'Gradiometers/WORDS')
#filename_fig = op.join(result_path, 'TOPO_grad_words.png')
#fig3.savefig(filename_fig, dpi=600)
#evoked_pic.copy().apply_baseline(baseline=(-0.1, 0))
#fig3=evoked_pic.copy().pick_types(meg='grad').plot_topo(title = 'Gradiometers/PICTURES')
#filename_fig = op.join(result_path, 'TOPO_grad_pic.png')
#fig3.savefig(filename_fig, dpi=600)
ERP_all=np.mean(evoked_pic.pick_types(meg='mag').get_data(),axis=0)
fig4=plt.figure()
plt.plot(evoked_pic.times,ERP_all)
filename_fig = op.join(result_path, 'ERP_all'+aaa+'.png')
fig4.savefig(filename_fig, dpi=600)
if __name__ == "__main__":
import sys
import os
import numpy as np
import mne
import matplotlib
import sys
#matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from mne.preprocessing import annotate_muscle_zscore
import os.path as op
from mne.preprocessing import ICA
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Scaler,
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
import sklearn.svm
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from init_y import *
from joblib import delayed, Parallel
Cat_all(int(sys.argv[1]))