def concatenate_run_dataset(sub_id = sub, proc='bipolar_montage', task = 'rest_baseline', preload = True):
subject = cifar_load_subject.Subject(name=sub_id, task = task, run='1')
fpath = subject.dataset_path(proc=proc, suffix='BP_montage', ext='.set')
raw = mne.io.read_raw_eeglab(fpath, preload=preload)
raw_1 = raw.copy()
subject = cifar_load_subject.Subject(name=sub_id, task = task, run='2')
fpath = subject.dataset_path(proc=proc, suffix='BP_montage', ext='.set')
raw = mne.io.read_raw_eeglab(fpath, preload=preload)
raw_2 = raw.copy()
raw_1.append([raw_2])
return raw_1
import seaborn as sns import helper_functions as fun import HFB_process as hf from scipy.io import loadmat #%% sub_id = 'DiAs' visual_chan_table = 'visual_channels_BP_montage.csv' proc = 'preproc' sfreq = 250; suffix = 'preprocessed_raw' ext = '.fif' categories = ['Rest', 'Face', 'Place'] subject = cf.Subject(name=sub_id) datadir = subject.processing_stage_path(proc=proc) # %% Plot spectral GC fres = 1024; fmax = sfreq/2; fname = sub_id + 'spectral_gc.mat' fpath = datadir.joinpath(fname) sGC = loadmat(fpath) f = sGC['f'] freqs = np.arange(start=0, stop= fmax, step = fmax/fres) (nchan, nchan, nfreq, ncat) = f.shape f_tot = np.zeros((nfreq, ncat))
proc = 'preproc'
for sub in subjects:
for task in tasks:
for run in runs:
sub = sub
task = task
run = run
suffix = 'bad_chans_removed_raw'
ext = '.fif'
suffix2save = 'bad_chans_removed'
ext2save = '.mat'
# %% Import data
subject = cifar_load_subject.Subject(name=sub, task=task, run=run)
fpath = subject.dataset_path(proc=proc, suffix=suffix, ext=ext)
raw = mne.io.read_raw_fif(fpath, preload=True)
dfelec = subject.df_electrodes_info()
# drop bad channels
bads = raw.info['bads']
raw_drop_bads = raw.copy().drop_channels(bads)
# Extract time series and save to matlab
time_series = raw_drop_bads.get_data()
fpath2save = subject.dataset_path(proc=proc,
#%%
sub_id = 'DiAs'
proc = 'preproc'
stage = '_BP_montage_preprocessed_raw.fif'
picks = ['LTo1-LTo2', 'LTo5-LTo6']
sfreq = 250
tmin = 0
tmax = 1.75
win_size = 0.200
step = 0.020
detrend = False
#%% Load data
subject = cf.Subject(sub_id)
datadir = subject.processing_stage_path(proc=proc)
visual_populations = subject.pick_visual_chan()
#%%
ts, time = hf.sliding_lfp(picks,
proc=proc,
stage=stage,
sub_id=sub_id,
tmin=tmin,
tmax=tmax,
win_size=win_size,
step=step,
detrend=detrend,
sfreq=sfreq)
t_postim = 1.75
tmin_prestim = -0.4
tmax_prestim = -0.1
tmin_postim = 0.1
tmax_postim = 0.5
alpha = 0.05
zero_method = 'pratt'
alternative = 'two-sided'
for sub in sub_id:
# Subject parameters
sub = sub
#%% Read preprocessed data
subject = cf.Subject(name=sub)
datadir = subject.processing_stage_path(proc=proc)
hfb_db = subject.load_data(proc=proc, stage=stage, epo=epo)
dfelec = subject.df_electrodes_info()
visual_populations = hf.hfb_to_visual_populations(
hfb_db,
dfelec,
tmin_prestim=tmin_prestim,
tmax_prestim=tmax_prestim,
tmin_postim=tmin_postim,
tmax_postim=tmax_postim,
alpha=alpha,
zero_method=zero_method)
# Create single subject dataframe
df_visual = pd.DataFrame.from_dict(visual_populations)
proc = 'preproc'
fname = sub_id + '_BP_montage_preprocessed_raw.fif'
tmin = 100
tmax = 102
l_freq = 70
band_size=20.0
l_trans_bandwidth= 10.0
h_trans_bandwidth= 10.0
filter_length='auto'
phase='minimum'
ichan = 6
figname = 'HFB_envelope_extraction.jpg'
figpath = Path.home().joinpath('projects','CIFAR','figures', figname)
#%% Load data
subject = cf.Subject()
fpath = subject.processing_stage_path(proc = proc)
fpath = fpath.joinpath(fname)
raw = mne.io.read_raw_fif(fpath, preload=True)
raw = raw.crop(tmin=tmin, tmax=tmax)
times = raw.times
#%% Test bands
bands = hf.freq_bands()
#%% Test HFB extraction
HFA = hf.extract_envelope(raw, l_freq = 60, band_size=100.0, l_trans_bandwidth= 10.0,
h_trans_bandwidth= 10.0, filter_length='auto', phase='minimum')
HFB = hf.extract_hfb(raw, l_freq=60.0, nband=6, band_size=20.0,
return raw_rest #%% Concatenate raw = concatenate_task_dataset(sub_id = sub) #%% Mark bad channels raw.plot(scalings=1e-4, duration =200) #%% Check psd raw.plot_psd(xscale = 'log') # %% Save dataset in fif and mat subject = cifar_load_subject.Subject(name=sub) fpath_save = subject.processing_stage_path(proc='preproc') dataset_save = sub + '_BP_montage_concatenated_bads_marked_raw.fif' fname_save = fpath_save.joinpath(dataset_save) raw.save(fname_save, overwrite=True) # Drop bad chans and save in mat format bads = raw.info['bads'] raw_drop_bads = raw.copy().drop_channels(bads) dataset_save = sub+ '_BP_montage_preprocessed_raw.fif' fname_save = fpath_save.joinpath(dataset_save) raw_drop_bads.save(fname_save, overwrite=True)
