# fig_path = os.path.abspath('/media/ravinderjit/Data_Drive/Data/Figures/BindingPilot/Monaural/'+subject)
fig_path_blinkprojs = os.path.abspath('/media/ravinderjit/Data_Drive/Data/Figures/BindingPilot/BlinkProjections')
if EEG_type =='Active':
data_path = os.path.join(data_loc, 'Active', subject)
else:
data_path = os.path.join(data_loc, 'Passive', subject)
fig_name = subject+'_'+EEG_type
title_base = subject+ ' ' + EEG_type + ' '
exclude = ['EXG3','EXG4','EXG5','EXG6','EXG7','EXG8']; #don't need these extra external channels that are saved
data_eeg,data_evnt = EEGconcatenateFolder(data_path+'/',nchans,refchans,exclude)
data_eeg.filter(l_freq=2,h_freq=120)
## blink removal
blinks_eeg = find_blinks(data_eeg, ch_name = ['A1'], thresh = 100e-6, l_trans_bandwidth=0.5, l_freq = 1.0)
scalings = dict(eeg=40e-6,stim=0.1)
blink_epochs = mne.Epochs(data_eeg,blinks_eeg,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
Projs_data = compute_proj_epochs(blink_epochs, n_grad=0,n_mag=0,n_eeg=8,verbose='DEBUG')
# data_eeg.add_proj(Projs_data)
# data_eeg.plot_projs_topomap()
from anlffr.spectral import mtplv
nchans = 34
refchans = ['EXG1', 'EXG2']
data_eeg = []
data_evnt = []
#data_loc = '/media/ravinderjit/Storage2/EEGdata'
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/Neural_CMR'
subject = 'S211_plus12dB'
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=300)
#%% 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,
'figOut':'\\Users\\racqu\\Documents\\Research\\Purdue\\HumanData\\Figs\\'} # define directories # define directories
read_data_params = {
'nchans': 34,
'refchans': ['EXG1', 'EXG2'],
'exclude': ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
}
if not op.isdir(dir_dict['figOut']):
mkdir(dir_dict['figOut'])
allFiles = glob(dir_dict['dataIn'] + '*.bdf')
plotYes = True
saveYes = False
# Loop through directories
# raw, eves= bs.importbdf(fileVar, refchans=read_data_params['refchans'], exclude=read_data_params['exclude'])
raw, eves= EEGconcatenateFolder(dir_dict['dataIn'], nchans=read_data_params['nchans'], fs_new=4e3, \
refchans=read_data_params['refchans'], exclude=read_data_params['exclude'])
# raw= mne.io.read_raw_edf(fileVar)
# raw.load_data()
# eves= mne.find_events(raw, shortest_event=1, mask=255)
# raw.filter(70, 1e3, phase='zero') # Not needed here as mtspec/mtplv have filter params
epochs = mne.Epochs(raw,
eves,
1,
tmin=-0.1,
proj=False,
tmax=1.2,
baseline=(-0.1, 0.0),
picks=31,
reject=dict(eeg=200e-6))
evoked = epochs.average()
params = dict(Fs=raw.info['sfreq'], tapers=[1, 1], fpass=[70, 1000], itc=0)
Mseq_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/TemporalCoding/mseqEEG_40_4096.mat'
Mseq_dat = sio.loadmat(Mseq_loc)
mseq = Mseq_dat['mseqEEG_4096']
mseq = mseq.astype(float)
#data_loc = '/media/ravinderjit/Storage2/EEGdata/'
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/TemporalCoding/AMFMmseq/'
subject = 'S211'
exclude = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
#don't need these extra external channels that are saved
datapath = os.path.join(data_loc, subject)
# datapath = '/media/ravinderjit/Data_Drive/Data/EEGdata/EFR'
data_eeg, data_evnt = EEGconcatenateFolder(datapath + '/', nchans, refchans,
exclude)
data_eeg.filter(l_freq=1, h_freq=80)
#%% Blink Removal
blinks = find_blinks(data_eeg,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
blink_epochs = mne.Epochs(data_eeg,
blinks,
998,
tmin=-0.25,
tmax=0.25,
proj=False,
baseline=(-0.25, 0),
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)
import mne
#import os
import numpy as np
import spectralAnalysis as spA
from anlffr import spectral
import scipy.io as sio
folder = 'Q394_111620'
data_loc = '/media/ravinderjit/Storage2/ChinCap/SAM_noise/' + folder + '/SAM_'
pathThing = '/'
nchans = 37
# refchans = ['A1','A2','A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16','A17','A18','A19',
# 'A20','A21','A22','A23','A29','A30','A31','A32']
refchans = ['EXG1', 'EXG2']
exclude = ['EXG6', 'EXG7', 'EXG8']
data_AM5, evnts_AM5 = EEGconcatenateFolder(data_loc + '4' + pathThing, nchans,
refchans, exclude)
data_AM5.filter(2, 40)
data_AM5.set_channel_types({'EXG4': 'eeg', 'EXG3': 'eeg', 'EXG5': 'eeg'})
#data_eeg.notch_filter(60)
#bad_chs = [0,5,8,9,16,24,25,26,27]
bad_chs = [23, 24, 25, 26, 27]
All_chs = np.arange(32)
channels = np.delete(All_chs, bad_chs)
bad_chs = [
'A24', 'A25', 'A26', 'A27', 'A28', 'EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5'
]
data_AM5.drop_channels(bad_chs)
data_AM5.set_eeg_reference(ref_channels='average')
scalings = dict(eeg=20e-6, stim=1)
nchans = 34;
refchans = ['EXG1','EXG2']
Subjects = ['S211_ABAB','S211_BABA']
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/Binding_20tones/'
exclude = ['EXG3','EXG4','EXG5','EXG6','EXG7','EXG8']; #don't need these extra external channels that are saved
subject = Subjects[0]
datapath = os.path.join(data_loc,subject)
data_eegAB,data_evnt = EEGconcatenateFolder(datapath+'/',nchans,refchans,exclude)
data_eegAB.filter(l_freq=1,h_freq=40)
datapath = os.path.join(data_loc,Subjects[1])
data_eegBA, data_evnt = EEGconcatenateFolder(datapath+'/',nchans,refchans,exclude)
data_eegBA.filter(l_freq=1,h_freq=40)
data_eegAB.info['bads'].append('A23')
data_eegBA.info['bads'].append('A23')
#%% Remove Blinks
blinks = find_blinks(data_eegAB,ch_name = ['A1'],thresh = 100e-6, l_trans_bandwidth = 0.5, l_freq =1.0)
blink_epochs = mne.Epochs(data_eegAB,blinks,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
def bdf2mat(froot, fs, Fs_new, hpf, t_stim, topchans, trial_name):
full_raw, full_eves = EEGconcatenateFolder(froot,
nchans=34,
refchans=['EXG1', 'EXG2'],
exclude=[],
fs_new=Fs_new)
event_id = {'Positive': 1}
epochs = mne.Epochs(full_raw,
full_eves,
event_id,
tmin=t_stim[0],
tmax=t_stim[1],
reject_tmax=1.3,
picks=topchans,
reject=dict(eeg=100e-6))
epochs.load_data()
epochs_filtered = epochs.filter(hpf, None)
pos_data = epochs_filtered.get_data()
event_id = {'Negative': 2}
epochs = mne.Epochs(full_raw,
full_eves,
event_id,
tmin=t_stim[0],
tmax=t_stim[1],
reject_tmax=1.3,
picks=topchans,
reject=dict(eeg=100e-6))
epochs.load_data()
epochs_filtered = epochs.filter(hpf, None)
neg_data = epochs_filtered.get_data()
neg_l = len(neg_data)
pos_l = len(pos_data)
length = (neg_l >= pos_l) * pos_l + (neg_l < pos_l) * neg_l
tot_arr = np.zeros(2 * length, dtype=np.object)
ind = 0
for i in range(0, length):
tot_arr[ind] = pos_data[i]
tot_arr[ind + 1] = neg_data[i]
ind = ind + 2
os.chdir(froot)
savemat(trial_name + '_Data_full.mat',
{(trial_name + '_tot_arr'): tot_arr})
###########################PLOT SPECTROGRAM/PLV####################################
x = np.add(neg_data[:length, :, :], pos_data[:length, :, :])
params = dict(Fs=4e3, tapers=[1, 1], fpass=[0, 1000], itc=0)
S_psd, N_psd, f_psd = mtspec(x, params)
mt_plv, f_plv = mtplv(x, params)
fig, ax = plt.subplots(num=2, figsize=(12, 8))
ax.plot(f_psd, np.subtract(S_psd, N_psd))
#ax.plot(f_psd, N_psd)
ax.grid(color='k', linestyle='-', linewidth=1)
ax.set_xlabel('Freq (Hz)', fontsize=18)
ax.set_ylabel('PSD', fontsize=18)
ax.set_xlim([70., 500.])
ax.set_title('psd all')
fig, ax = plt.subplots(num=1, figsize=(12, 8))
ax.plot(f_plv, mt_plv)
#ax.plot(f_psd, N_psd)
ax.grid(color='k', linestyle='-', linewidth=1)
ax.set_xlabel('Freq (Hz)', fontsize=18)
ax.set_ylabel('PLV', fontsize=18)
ax.set_xlim([70., 500.])
ax.set_title('PLV all')
#import numpy as np
#import scipy as sp
import pylab as pl
import matplotlib.pyplot as plt
import scipy.io as sio
import mne
from anlffr.preproc import find_blinks
from EEGpp import EEGconcatenateFolder
from mne.preprocessing.ssp import compute_proj_epochs
Subject = 'Rav'
nchans = 34;
refchans = ['EXG1','EXG2']
direct_IPDITD = '/media/ravinderjit/Data_Drive/EEGdata/Binaural/IPD_ITD/'
Bin_eeg, Bin_evnt = EEGconcatenateFolder(direct_IPDITD+Subject+'/',nchans,refchans)
Bin_eeg.filter(70,1500)
blinks = find_blinks(Bin_eeg, ch_name = ['A1'], thresh = 100e-6, l_trans_bandwidth=0.5,l_freq=1.0)
scalings = dict(eeg=40e-6)
blink_epochs = mne.Epochs(Bin_eeg,blinks,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
Proj = compute_proj_epochs(blink_epochs,n_grad=0,n_mag=0,n_eeg=8,verbose='DEBUG')
#Bin_eeg.add_proj(Proj)
#Bin_eeg.plot_projs_topomap()
eye_projs = [Proj[0],Proj[2]]
Bin_eeg.add_proj(eye_projs)
Bin_eeg.plot_projs_topomap()
Bin_eeg.plot(events=blinks,scalings=scalings,show_options=True,title = 'IPD ITD')
from EEGpp import EEGconcatenateFolder
import mne
from anlffr import spectral
import os
from scipy import linalg
#data_loc = r'H:\ChinCap\090720\'
data_loc = r'H:\ChinCap\082020\\'
data_loc = '/home/ravinderjit/Documents/ChinCapData/092320/'
stim_type = 'tone_4k_' #'click_' tone_4k_
pathThing = '/'
nchans = 34
refchans = ['A1', 'A2']
refchans = ['EXG1']
exclude = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
data_eeg, data_evnt = EEGconcatenateFolder(
data_loc + stim_type + '80' + pathThing, nchans, refchans, exclude)
data_eeg.filter(300, 3000)
#data_eeg.notch_filter(60)
scalings = dict(eeg=20e-6, stim=1)
#data_eeg.plot(events = data_evnt, scalings=scalings,show_options=True)
data_eeg.plot_psd(picks=[31, 32, 33])
epochs = mne.Epochs(data_eeg, data_evnt, [255], tmin=-0.005, tmax=0.01)
evoked_80 = epochs.average()
channels = list(range(24))
channels.extend([29, 30, 31])
channels.remove(21)
channels.remove(20)
channels.remove(0)
evoked_80.plot(picks=channels, titles='80')
import numpy as np
from scipy.signal import periodogram
from anlffr import spectral
import scipy.io as sio
folder = 'Chin_CMRrandMod_anesth/'
#data_loc = '/media/ravinderjit/Storage2/ChinCap/'
data_loc = '/media/ravinderjit/Data_Drive/Data/ChinCap/'
#data_loc = '/home/ravinderjit/Documents/ChinCapData/'
nchans = 35
# refchans = ['A1','A2','A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16','A17','A18','A19',
# 'A20','A21','A22','A23','A29','A30','A31','A32']
refchans = ['EXG1','EXG2']
exclude = ['EXG4','EXG5','EXG6','EXG7','EXG8']
data_eeg,evnts_eeg = EEGconcatenateFolder(data_loc + folder ,nchans,refchans,exclude)
data_eeg.filter(1,300)
data_eeg.set_channel_types({'EXG3':'eeg'})
bad_chs = ['A1','A25','A26','A27','A28','EXG3']#,'EXG1','EXG2','A20']
data_eeg.drop_channels(bad_chs)
#data_eeg.set_eeg_reference(ref_channels='average')
scalings = dict(eeg=20e-6,stim=1)
data_eeg.plot(events = evnts_eeg, scalings=scalings,show_options=True)
epochs_all = []
for m in np.arange(4):
epochs_m = mne.Epochs(data_eeg,evnts_eeg,[m+1],tmin=-0.50,tmax=4.5,baseline=(-0.2,0))#,reject=dict(eeg=200e-6))
evoked_m = epochs_m.average()
evoked_m.plot(titles = str(m+1))
#Corr_inds{7} = [1,4,7,10,13,16];
#Corr_inds{8} = 3:16;
nchans = 34
refchans = ['EXG1', 'EXG2']
data_eeg = []
data_evnt = []
#direct_ = '../../../Data/EEGdata/Binding/BindingPassivePilot'
direct_ = '/media/ravinderjit/Data_Drive/Dropbox/Lab/Data/EEGdata/Binding/BindingBehPilot'
exclude = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
#don't need these extra external channels that are saved
data_eeg, data_evnt = EEGconcatenateFolder(direct_ + '/', nchans, refchans,
exclude)
data_eeg.filter(l_freq=1, h_freq=100)
## blink removal
blinks_eeg = find_blinks(data_eeg,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
scalings = dict(eeg=40e-6, stim=0.1)
blink_epochs = mne.Epochs(data_eeg,
blinks_eeg,
998,
tmin=-0.25,
tmax=0.25,
nchans = 34;
refchans = ['EXG1','EXG2']
data_eeg = [];
data_evnt = [];
direct_ = '/media/ravinderjit/Data_Drive/EEGdata/Binding/'
exclude = ['EXG3','EXG4','EXG5','EXG6','EXG7','EXG8']; #don't need these extra external channels that are saved
data_eeg,data_evnt = EEGconcatenateFolder(direct_+'/',nchans,refchans,exclude)
data_eeg.filter(l_freq=1,h_freq=130)
## blink removal
blinks_eeg = find_blinks(data_eeg, ch_name = ['A1'], thresh = 100e-6, l_trans_bandwidth=0.5, l_freq = 1.0)
scalings = dict(eeg=40e-6,stim=0.1)
blink_epochs = mne.Epochs(data_eeg,blinks_eeg,998,tmin=-0.25,tmax=0.25,proj=False,
baseline=(-0.25,0),reject=dict(eeg=500e-6))
Projs_data = compute_proj_epochs(blink_epochs, n_grad=0,n_mag=0,n_eeg=8,verbose='DEBUG')
#data_eeg.add_proj(Projs_data)
#data_eeg.plot_projs_topomap()
# Mseq = decimate(Mseq,2,axis=0)
#fix issues due to filtering from downsampling ... data is sampled at 2048
# Mseq[Mseq<0] = -1
# Mseq[Mseq>0] = 1
Subjects = [
'S001', 'S132', 'S203', 'S204', 'S205', 'S206', 'S207', 'S208', 'S211'
]
Subject = 'S211'
exclude = ['EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
#%% Load data
# with open(os.path.join(data_loc, Subject+'_DynBin.pickle'),'rb') as f:
# IAC_epochs, ITD_epochs = pickle.load(f)
IAC_eeg, IAC_evnt = EEGconcatenateFolder(direct_IAC + Subject + '/', nchans,
refchans, exclude)
ITD_eeg, ITD_evnt = EEGconcatenateFolder(direct_ITD + Subject + '/', nchans,
refchans, exclude)
IAC_eeg.filter(1, 1000)
ITD_eeg.filter(1, 1000)
#%% Blink removal
blinks_IAC = find_blinks(IAC_eeg,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
blinks_ITD = find_blinks(ITD_eeg,
ch_name=['A1'],
thresh=100e-6,
nchans = 34;
refchans = ['EXG1','EXG2']
data_eeg = [];
data_evnt = [];
#data_loc = '/media/ravinderjit/Storage2/EEGdata'
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/'
subject = 'CMR_TempCoherence'
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=150)
#%% Blink Removal
blinks = find_blinks(data_eeg,ch_name = ['A1'],thresh = 100e-6, l_trans_bandwidth = 0.5, l_freq =1.0)
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=data_evnt)
#%% Plot Data
data_evnt = []
Mseq_loc = '/media/ravinderjit/Data_Drive/Stim_Analysis/Stimuli/TemporalCoding/Stim_Dev/mseqEEG_4096.mat'
Mseq_dat = sio.loadmat(Mseq_loc)
mseq = Mseq_dat['mseqEEG_4096']
mseq = mseq.astype(float)
data_loc = '/media/ravinderjit/Data_Drive/Data/EEGdata/TemporalCoding/AMmseq'
subject = 'S233'
exclude = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
#don't need these extra external channels that are saved
datapath = os.path.join(data_loc, subject)
# datapath = '/media/ravinderjit/Data_Drive/Data/EEGdata/EFR'
data_eeg, data_evnt = EEGconcatenateFolder(datapath + '/', nchans, refchans,
exclude)
data_eeg.filter(l_freq=1, h_freq=1500)
#%% Blink Removal
blinks = find_blinks(data_eeg,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
blink_epochs = mne.Epochs(data_eeg,
blinks,
998,
tmin=-0.25,
tmax=0.25,
proj=False,
baseline=(-0.25, 0),
import mne
#import os
import numpy as np
from scipy.signal import periodogram
from anlffr import spectral
import scipy.io as sio
folder = 'Chin_SAM_tmtf/'
#data_loc = '/media/ravinderjit/Storage2/ChinCap/'
data_loc = '/home/ravinderjit/Documents/ChinCapData/'
nchans = 35
# refchans = ['A1','A2','A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16','A17','A18','A19',
# 'A20','A21','A22','A23','A29','A30','A31','A32']
refchans = ['EXG1', 'EXG2']
exclude = ['EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
data_eeg, evnts_eeg = EEGconcatenateFolder(data_loc + folder, nchans, refchans,
exclude)
data_eeg.filter(5, 3500)
data_eeg.set_channel_types({'EXG3': 'eeg'})
AMf = [20, 30, 40, 55, 70, 90, 110, 170, 250, 400, 600, 800, 1000, 3000]
bad_chs = [
'A1', 'A25', 'A26', 'A27', 'A28', 'EXG3', 'EXG1', 'EXG2', 'A20', 'A21',
'A22', 'A30'
]
data_eeg.info['bads'] = bad_chs
#data_eeg.drop_channels(bad_chs)
data_eeg.set_eeg_reference(ref_channels='average')
scalings = dict(eeg=20e-6, stim=1)
data_eeg.plot(events=evnts_eeg, scalings=scalings, show_options=True)
os.path.join(pickle_active_loc,
subject + '_AMmseq10bits_Active.pickle'),
'rb') as file:
[t, Tot_trials, Ht, Htnf, info_obj, ch_picks] = pickle.load(file)
A_Tot_trials_act.append(Tot_trials)
num_nfs = 5
for subject in Subjects:
print('On Subject ................... ' + subject)
#%% Load data and filter
datapath = os.path.join(data_loc, subject)
data_eeg, data_evnt = EEGconcatenateFolder(datapath + '/', nchans,
refchans, exclude)
data_eeg.filter(l_freq=1, h_freq=500)
#%% Remove bad channels
if subject == 'S207':
data_eeg.info['bads'].append('A15')
#%% Blink Removal
blinks = find_blinks(data_eeg,
ch_name=['A1'],
thresh=100e-6,
l_trans_bandwidth=0.5,
l_freq=1.0)
blink_epochs = mne.Epochs(data_eeg,
blinks,
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,