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NEOS_stcsFactorialDesign.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 17 11:51:16 2023
@author: fm02
"""
import sys
import os
from os import path
import numpy as np
import pandas as pd
import mne
os.chdir("/home/fm02/MEG_NEOS/NEOS")
import NEOS_config as config
import re
ave_path = path.join(config.data_path, "AVE")
stc_path = path.join(config.data_path, "stcs")
snr = 3.
lambda2 = 1. / snr ** 2
# orientation = 'normal'
# conditions = [['Predictable', 'Unpredictable'], ['Abstract', 'Concrete']]
# conditions = ['abspred', 'absunpred', 'concpred', 'concunpred']
conditions = ['Predictable', 'Unpredictable', 'Abstract', 'Concrete']
def compute_stcs(sbj_id, method="MNE", inv_suf='', orientation=None):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
inv_fname = path.join(sbj_path, subject + f'_EEGMEG{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
evs=dict()
for condition in conditions:
fname = path.join(ave_path, f"{subject}_evokeds_nochan_4_8.fif")
evokeds = mne.read_evokeds(fname)
ev_0 = list()
ev_1 = list()
for e, evoked in enumerate(evokeds):
if re.match(f'([a-z]*\/)?{condition[0]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_0.append(evoked)
elif re.match(f'([a-z]*\/)?{condition[1]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_1.append(evoked)
evs[condition[0]] = mne.combine_evoked(ev_0, weights='nave')
evs[condition[1]] = mne.combine_evoked(ev_1, weights='nave')
for ev in evs.keys():
stc = mne.minimum_norm.apply_inverse(evs[ev], inverse_operator,
lambda2, method=method,
pick_ori=orientation, verbose=True)
if len(inv_suf)==0:
stc_fname = path.join(stc_path, f"{subject}_stc_{ev}_{method}")
elif len(inv_suf)>0:
stc_fname = path.join(stc_path, f"{subject}_stc_{ev}_{method}_{inv_suf}")
stc.save(stc_fname)
def compute_stcs_dropbads(sbj_id, method="MNE", inv_suf='', orientation=None):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
inv_fname = path.join(sbj_path, subject + f'_EEGMEG{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
evs=dict()
for condition in conditions:
fname = path.join(ave_path, f"{subject}_evokeds_dropbads.fif")
evokeds = mne.read_evokeds(fname)
ev_0 = list()
ev_1 = list()
for e, evoked in enumerate(evokeds):
if re.match(f'([a-z]*\/)?{condition[0]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_0.append(evoked)
elif re.match(f'([a-z]*\/)?{condition[1]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_1.append(evoked)
evs[condition[0]] = mne.combine_evoked(ev_0, weights='nave')
evs[condition[1]] = mne.combine_evoked(ev_1, weights='nave')
for ev in evs.keys():
stc = mne.minimum_norm.apply_inverse(evs[ev], inverse_operator,
lambda2, method=method,
pick_ori=orientation, verbose=True)
if len(inv_suf)==0:
stc_fname = path.join(stc_path, f"{subject}_stc_{ev}_{method}")
elif len(inv_suf)>0:
stc_fname = path.join(stc_path, f"{subject}_stc_{ev}_{method}_{inv_suf}")
stc.save(stc_fname)
def stcs_inlabel_dropbads(sbj_id, method="MNE", inv_suf='', orientation=None, mode_avg='mean_flip'):
labels_path = path.join(config.data_path, "my_ROIs")
lATL = mne.read_label(path.join(labels_path, 'l_ATL_fsaverage-lh.label'),
subject='fsaverage')
rATL = mne.read_label(path.join(labels_path, 'r_ATL_fsaverage-rh.label'),
subject='fsaverage')
PVA = mne.read_label(path.join(labels_path, 'PVA_fsaverage-lh.label'),
subject='fsaverage')
IFG = mne.read_label(path.join(labels_path, 'IFG_fsaverage-lh.label'),
subject='fsaverage')
AG = mne.read_label(path.join(labels_path, 'AG_fsaverage-lh.label'),
subject='fsaverage')
PTC = mne.read_label(path.join(labels_path, 'PTC_fsaverage-lh.label'),
subject='fsaverage')
rois = [lATL,
rATL,
PVA,
IFG,
AG,
PTC]
rois_lab = ['lATL',
'rATL',
'PVA',
'IFG',
'AG',
'PTC']
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
inv_fname = path.join(sbj_path, subject + f'_EEGMEG{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
evs=dict()
for condition in conditions:
fname = path.join(ave_path, f"{subject}_evokeds_dropbads.fif")
evokeds = mne.read_evokeds(fname)
[evoked.resample(250, npad='auto') for evoked in evokeds]
ev_0 = list()
ev_1 = list()
for e, evoked in enumerate(evokeds):
if re.match(f'([a-z]*\/)?{condition[0]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_0.append(evoked)
elif re.match(f'([a-z]*\/)?{condition[1]}(\/[a-z]*)?', evoked.comment, re.IGNORECASE):
ev_1.append(evoked)
evs[condition[0]] = mne.combine_evoked(ev_0, weights='nave')
evs[condition[1]] = mne.combine_evoked(ev_1, weights='nave')
rois_subject = mne.morph_labels(rois, subject_to=subject,
subject_from='fsaverage',
subjects_dir=config.subjects_dir)
for ev in evs.keys():
stc = mne.minimum_norm.apply_inverse(evs[ev], inverse_operator,
lambda2, method=method,
pick_ori=orientation, verbose=True)
src = inverse_operator["src"]
activity = stc.extract_label_time_course(rois_subject, src, mode=mode_avg)
activity = pd.DataFrame(activity.T, columns=rois_lab)
activity.to_csv(path.join(ave_path, "in_labels", f"{sbj_id}_{ev}_in_labels.csv"))
def save_stcs_condition(fpath_evoked, MEGonly, inverse_operator, method,
inv_suf, orientation=None):
evoked = mne.read_evokeds(fpath_evoked)[0]
if MEGonly:
evoked = evoked.pick_types(meg=True, eeg=False, exclude='bads')
stc = mne.minimum_norm.apply_inverse(evoked, inverse_operator,
lambda2, method=method,
pick_ori=orientation, verbose=True)
# THIS WORKS ONLY IF YOU USE THE STANDARD NAMING, USE WITH CARE
# ideally you'd want to use evoked.comment
subject, condition = fpath_evoked.split('/')[-1].split('_')[0:2]
stc_fname = path.join(stc_path, f"{subject}_stc_{condition}_{method}_{inv_suf}")
stc.save(stc_fname)
return stc
def save_unfold_stcs_condition(fpath_evoked, MEGonly, inverse_operator, method,
inv_suf, orientation=None):
evoked = mne.read_evokeds(fpath_evoked)[0]
#evoked.apply_baseline(baseline=(-0.152,-0.020))
if MEGonly:
evoked = evoked.pick_types(meg=True, eeg=False, exclude='bads')
stc = mne.minimum_norm.apply_inverse(evoked, inverse_operator,
lambda2, method=method,
pick_ori=orientation, verbose=True)
# THIS WORKS ONLY IF YOU USE THE STANDARD NAMING, USE WITH CARE
# ideally you'd want to use evoked.comment
subject, condition = fpath_evoked.split('/')[-1].split('_')[0:2]
stc_fname = path.join(stc_path, f"{subject}_unfold_stc_{condition}_{method}_{inv_suf}")
stc.save(stc_fname, overwrite=True)
return stc
def compute_evoked_condition_stcs(sbj_id, MEGonly=False, method="eLORETA", inv_suf='EEGMEGauto_dropbads',
orientation=None):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
# PARTICIPANT 12 PROBLEMS WITH EEG DURING RECORDING
if sbj_id==12:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
else:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
for condition in ['Abstract', 'Concrete', 'Predictable', 'Unpredictable']:
filename = path.join(ave_path, f"{subject}_{condition}_evokeds_dropbads-ave.fif")
save_stcs_condition(filename, MEGonly, inverse_operator, method, inv_suf, orientation)
def compute_unfold_evoked_condition_stcs(sbj_id, MEGonly=False, method="eLORETA", inv_suf='EEGMEGauto_dropbads',
orientation=None):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
# PARTICIPANT 12 PROBLEMS WITH EEG DURING RECORDING
if sbj_id==12:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
else:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
for condition in ['Abstract', 'Concrete', 'Predictable', 'Unpredictable']:
filename = path.join(ave_path, f"{subject}_{condition}_unfold_evoked-ave.fif")
save_unfold_stcs_condition(filename, MEGonly, inverse_operator, method, inv_suf, orientation)
def stcs_inlabel_from_stc(sbj_id, method="eLORETA", inv_suf='EEGMEGauto_dropbads', mode_avg='mean'):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
labels_path = path.join(config.data_path, "my_ROIs")
lATL = mne.read_label(path.join(labels_path, 'l_ATL_fsaverage-lh.label'),
subject='fsaverage')
rATL = mne.read_label(path.join(labels_path, 'r_ATL_fsaverage-rh.label'),
subject='fsaverage')
PVA = mne.read_label(path.join(labels_path, 'PVA_fsaverage-lh.label'),
subject='fsaverage')
IFG = mne.read_label(path.join(labels_path, 'IFG_fsaverage-lh.label'),
subject='fsaverage')
AG = mne.read_label(path.join(labels_path, 'AG_fsaverage-lh.label'),
subject='fsaverage')
PTC = mne.read_label(path.join(labels_path, 'PTC_fsaverage-lh.label'),
subject='fsaverage')
rois = [lATL,
rATL,
PVA,
IFG,
AG,
PTC]
rois_lab = ['lATL',
'rATL',
'PVA',
'IFG',
'AG',
'PTC']
if sbj_id==12:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
else:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
src = inverse_operator["src"]
subject = str(sbj_id)
rois_subject = mne.morph_labels(rois, subject_to=subject,
subject_from='fsaverage',
subjects_dir=config.subjects_dir)
stc = dict.fromkeys(conditions)
activity = dict.fromkeys(conditions)
for condition in conditions:
stc[condition] = mne.read_source_estimate(os.path.join(stc_path, f"{subject}_stc_{condition}_{method}_{inv_suf}"))
stc[condition].resample(250)
activity[condition] = stc[condition].extract_label_time_course(rois_subject, src=src, mode=mode_avg)
activity[condition] = pd.DataFrame(activity[condition].T, columns=rois_lab, index=stc[condition].times)
activity[condition].to_csv(path.join(stc_path, "in_labels", f"{sbj_id}_{condition}_{inv_suf}_in_labels.csv"))
def stcs_unfold_inlabel_from_stc(sbj_id, method="eLORETA", inv_suf='EEGMEGauto_dropbads', mode_avg='mean'):
subject = str(sbj_id)
sbj_path = path.join(config.data_path, config.map_subjects[sbj_id][0])
labels_path = path.join(config.data_path, "my_ROIs")
lATL = mne.read_label(path.join(labels_path, 'l_ATL_fsaverage-lh.label'),
subject='fsaverage')
rATL = mne.read_label(path.join(labels_path, 'r_ATL_fsaverage-rh.label'),
subject='fsaverage')
PVA = mne.read_label(path.join(labels_path, 'PVA_fsaverage-lh.label'),
subject='fsaverage')
IFG = mne.read_label(path.join(labels_path, 'IFG_fsaverage-lh.label'),
subject='fsaverage')
AG = mne.read_label(path.join(labels_path, 'AG_fsaverage-lh.label'),
subject='fsaverage')
PTC = mne.read_label(path.join(labels_path, 'PTC_fsaverage-lh.label'),
subject='fsaverage')
rois = [lATL,
rATL,
PVA,
IFG,
AG,
PTC]
rois_lab = ['lATL',
'rATL',
'PVA',
'IFG',
'AG',
'PTC']
if sbj_id==12:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
else:
inv_fname = path.join(sbj_path, subject + f'_{inv_suf}-inv.fif')
inverse_operator = mne.minimum_norm.read_inverse_operator(inv_fname)
src = inverse_operator["src"]
subject = str(sbj_id)
rois_subject = mne.morph_labels(rois, subject_to=subject,
subject_from='fsaverage',
subjects_dir=config.subjects_dir)
stc = dict.fromkeys(conditions)
activity = dict.fromkeys(conditions)
for condition in conditions:
stc[condition] = mne.read_source_estimate(os.path.join(stc_path, f"{subject}_unfold_stc_{condition}_{method}_{inv_suf}"))
activity[condition] = stc[condition].extract_label_time_course(rois_subject, src=src, mode=mode_avg)
activity[condition] = pd.DataFrame(activity[condition].T, columns=rois_lab, index=stc[condition].times)
activity[condition].to_csv(path.join(stc_path, "in_labels", f"{sbj_id}_unfold_{condition}_{inv_suf}_in_labels.csv"))
# if len(sys.argv) == 1:
# sbj_ids = [1,2,3,5,6,8,9,10,11,12,13,14,15,16,17,18,19,
# 21,22,23,24,25,26,27,28,29,30]
# else:
# # get list of subjects IDs to process
# sbj_ids = [int(aa) for aa in sys.argv[1:]]
# for ss in sbj_ids:
# compute_stcs(ss)