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)
