nchans = 34;
refchans = ['EXG1','EXG2']
data_eeg = [];
data_evnt = [];
#data_loc = '/media/ravinderjit/Storage2/EEGdata'
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/CMR_active/CMRactive_10Hz'
subject = 'SVarsha'
exclude = ['EXG3','EXG4','EXG5','EXG6','EXG7','EXG8']
datapath = os.path.join(data_loc,subject)
data_eeg,data_evnt = EEGconcatenateFolder(datapath+'/',nchans,refchans,exclude)
data_eeg.filter(l_freq=1,h_freq=100)
#%% Blink Removal
blinks = find_blinks(data_eeg,ch_name = ['A1'],thresh = 100e-6, l_trans_bandwidth = 0.5, l_freq =1.0, h_freq=10)
blink_epochs = mne.Epochs(data_eeg,blinks,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
Projs = compute_proj_epochs(blink_epochs,n_grad=0,n_mag=0,n_eeg=8,verbose='DEBUG')
blink_proj = Projs[0]
data_eeg.add_proj(blink_proj)
data_eeg.plot_projs_topomap()
data_eeg.plot(events=blinks,show_options=True)
#%% Plot
labels = ['40 codev','40 comod']
refchans = None
direct_IAC = '/media/ravinderjit/Data_Drive/Data/EEGdata/BinauralMseq_reCollect/StandardWay/'
direct_Mseq = '/media/ravinderjit/Data_Drive/Data/EEGdata/DynamicBinaural/Mseq_4096fs_compensated.mat'
Mseq_mat = sio.loadmat(direct_Mseq)
Mseq = Mseq_mat['Mseq_sig'].T
Mseq = Mseq.astype(float)
exclude = ['EXG1','EXG2','EXG3','EXG4','EXG5','EXG6','EXG7','EXG8']; #don't need these extra external channels that are saved
IAC_eeg,IAC_evnt = EEGconcatenateFolder(direct_IAC+Subject+'/',nchans,refchans,exclude)
IAC_eeg.filter(1,40)
#%% blink removal
blinks_IAC = find_blinks(IAC_eeg, ch_name = ['A1'], thresh = 100e-6, l_trans_bandwidth=0.5, l_freq = 1.0)
scalings = dict(eeg=40e-6)
blinkIAC_epochs = mne.Epochs(IAC_eeg,blinks_IAC,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
Projs_IAC = compute_proj_epochs(blinkIAC_epochs, n_grad=0,n_mag=0,n_eeg=8,verbose='DEBUG')
eye_projsIAC = [Projs_IAC[0],Projs_IAC[1]]
IAC_eeg.add_proj(eye_projsIAC)
IAC_eeg.plot_projs_topomap()
import scipy.io as sio
import os
import pickle
import mne
from anlffr.preproc import find_blinks
from EEGpp import EEGconcatenateFolder
from mne.preprocessing.ssp import compute_proj_epochs
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/Sentences/'
subject = 'S211'
refchans = ['EXG1', 'EXG2']
nchans = 34
exclude = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
sentEEG, sent_evnt = EEGconcatenateFolder(data_loc + subject + '/', nchans,
refchans, exclude)
sentEEG.filter(1, 75)
blinks = find_blinks(sentEEG,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
blink_epochs = mne.Epochs(sentEEG,
blinks,
998,
tmin=-0.25,
tmax=0.25,
proj=False,
data_sumMasker, _ = summ(data_masker_pos, data_masker_neg)
return evoked_pos, evoked_neg, data_sumMasker, data_masker_diff, data_prob_diff, data_adpt_diff, numtrials_adpt
########################################################################################################################################################
froot = "C:\\Users\\racqu\\Documents\\Research\\Purdue\\HumanData\\AS\\SQ50\\"
fs = 16384
topchans = [31] #CHANGE AS NEEDED
bdfname = froot + bdf
full_raw, full_eves = EEGconcatenateFolder(froot,
nchans=34,
refchans=['EXG1', 'EXG2'],
exclude=[],
fs_new=4e3)
event_id = {'Positive': 1}
epochs = mne.Epochs(full_raw,
full_eves,
event_id,
tmin=0.0,
tmax=1.5,
reject_tmax=1.3,
picks=31,
reject=dict(eeg=100e-6))
epochs.load_data()
epochs_filtered = epochs.filter(70, None)