sub = dict(loc='workstation', id=i)
param = get_subject_info_wmConfidence(sub)
#get the epoched data
epochs = mne.read_epochs(
fname=param['fblocked'],
preload=True) #this is loaded in with the metadata
epochs.set_eeg_reference(['RM'])
epochs.apply_baseline((-.25, 0)) #baseline 250ms prior to feedback
epochs.resample(500) #resample to 500Hz
ntrials = len(epochs)
#will do an automated process of looking for trials with heightened variance (noise) and output which trials to keep
_, keeps = plot_AR(epochs,
method='gesd',
zthreshold=1.5,
p_out=.1,
alpha=.05,
outlier_side=1)
plt.close()
keeps = keeps.flatten()
discards = np.ones(len(epochs), dtype='bool')
discards[keeps] = False
epochs = epochs.drop(
discards) #first we'll drop trials with excessive noise in the EEG
epochs = epochs['DTcheck == 0 and clickresp == 1']
print('a total of %d trials have been dropped for this subjects' %
(ntrials - len(epochs)))
glmdata = glm.data.TrialGLMData(data=epochs.get_data(),
cuelocked = cuelocked.pick_types(
eeg=True, misc=True
) #if you don't do this, and run the gesd, then it can fail if the EOG variance makes trialwise variance estimation terrible
#we don't need the eogs at this point anyways because we ica'd out blinks etc, so its just extra data to take up space
bdata = cuelocked.metadata
prevtrlerr = bdata.shift(1).absrdif.to_numpy()
bdata['prevtrlerr'] = bdata.shift(1).absrdif.to_numpy()
bdata['prevtrlconf'] = bdata.shift(1).confwidth.to_numpy()
cuelocked.metadata = bdata
#will do an automated process of looking for trials with heightened variance (noise) and output which trials to keep
_, keeps = plot_AR(cuelocked,
method='gesd',
zthreshold=1.5,
p_out=.1,
alpha=.05,
outlier_side=1)
keeps = keeps.flatten()
discards = np.ones(len(cuelocked), dtype='bool')
discards[keeps] = False
cuelocked = cuelocked.drop(
discards
) #first we'll drop trials with excessive noise in the EEG
#now we'll drop trials with behaviour problems (reaction time +/- 2.5 SDs of mean, didn't click to report orientation)
cuelocked = cuelocked['DTcheck == 0 and clickresp == 1']
cuelocked.set_eeg_reference(ref_channels=[
'RM'
]) #re-reference to average of the two mastoids
bdata2 = pd.read_csv(param['behaviour_blinkchecked2'], index_col=None)
bdata2['prevtrlconfdiff'] = bdata2.confdiff.shift(
1
) #write down on each trial what the previous trial error awareness was
bdata2['nexttrlconfdiff'] = bdata2.confdiff.shift(
-1
) #write down on each trial what the next trials error awareness is (used in glm)
arraylocked1.metadata = bdata1
arraylocked2.metadata = bdata2
# arraylocked = mne.concatenate_epochs([arraylocked1, arraylocked2]) #combine the epoched data with aligned metadata
_, keeps = plot_AR(deepcopy(arraylocked1).pick_types(eeg=True),
method='gesd',
zthreshold=1.5,
p_out=.1,
alpha=.05,
outlier_side=1)
keeps = keeps.flatten()
discards = np.ones(len(arraylocked1), dtype='bool')
discards[keeps] = False
arraylocked1 = arraylocked1.drop(
discards) #first we'll drop trials with excessive noise in the EEG
#now we'll drop trials with behaviour problems (reaction time +/- 2.5 SDs of mean, didn't click to report orientation)
arraylocked1 = arraylocked1['DTcheck == 0 and clickresp == 1']
_, keeps = plot_AR(deepcopy(arraylocked2).pick_types(eeg=True),
method='gesd',
zthreshold=1.5,
print('\n\nworking on subject ' + str(i) + '\n\n')
sub = dict(loc='workstation', id=i)
param = get_subject_info_wmConfidence(sub)
probelocked = mne.epochs.read_epochs(fname=param['probelocked'],
preload=True) #read raw data
probelocked.resample(
100
) #downsample to 250Hz so don't overwork the workstation (100hz should be fine for frequencies up to 50Hz in brain)
#will do an automated process of looking for trials with heightened variance (noise) and output which trials to keep
if i != 18:
_, keeps = plot_AR(
probelocked,
method='gesd',
zthreshold=1.5,
p_out=.1,
alpha=.05,
outlier_side=1) #this fails on subject 18 for some fking reason
keeps = keeps.flatten()
discards = np.ones(len(probelocked), dtype='bool')
discards[keeps] = False
probelocked = probelocked.drop(
discards) #first we'll drop trials with excessive noise in the EEG
#now we'll drop trials with behaviour problems (reaction time +/- 2.5 SDs of mean, didn't click to report orientation)
probelocked = probelocked[
'DTcheck == 0 and clickresp == 1 and arraycueblink == 0'] #also exclude trials where blinks happened in the array or cue period
probelocked.set_eeg_reference(
ref_channels=['RM']) #re-reference to average of the two mastoids
#bad channels get marked and cant concatenate epochs unless these are the same
if cuelocked1.info['bads'] != []:
cuelocked1.interpolate_bads(reset_bads=True)
if cuelocked2.info['bads'] != []:
cuelocked2.interpolate_bads(reset_bads=True)
cuelocked = mne.concatenate_epochs(
[cuelocked1,
cuelocked2]) #combine the epoched data with aligned metadata
#trial rejection here
#step1 - automated gesd removal
#step 2, just check these epochs to make sure some haven't slipped through the net by accident. looking for more catastrophic failures, or noise in baseline
_, keeps = plot_AR(cuelocked.pick_types(eeg=True),
method='gesd',
zthreshold=1.5,
p_out=.1,
alpha=.05,
outlier_side=1)
keeps = keeps.flatten()
plt.close()
discards = np.ones(len(cuelocked), dtype='bool')
discards[keeps] = False
cuelocked = cuelocked.drop(
discards) #first we'll drop trials with excessive noise in the EEG
cuelocked = cuelocked[
'DTcheck == 0 and clickresp == 1'] #the last trial of the session doesn't have a following trial!
#now go through manually
# cuelocked.plot(n_channels=62, scalings = dict(eeg=200e-6), n_epochs=3)
