dat2use = deepcopy(data)
elif stat == 'tstat':
dat2use = deepcopy(data_t)
else:
if stat == 'beta':
dat2use = deepcopy(data_baselined)
elif stat == 'tstat':
dat2use = deepcopy(data_baselined_t)
dat2plot = deepcopy(dat2use)
gave = mne.grand_average(dat2plot[contrast])
plot_t = True
if plot_t:
gave.data = toverparam(dat2plot[contrast])
# gave.plot_joint(title = '%s, stat = %s'%(contrast, stat),
# timefreqs = timefreqs_alpha,
# vmin = topovmin['tstat'], vmax = topovmin['tstat']*-1,
# topomap_args = dict(outlines='head',
# extrapolate='head',
# contours=0,
# vmin = topovmin['tstat'],
# vmax = topovmin['tstat']*-1))
#run cluster test
#get data into dataframe first, for the channels we want
if 'right' in contrast or contrast == 'crvsn':
contrachans = visleftchans
ipsichans = visrightchans
for mask in masks_incorr:
ax.hlines(
y=-3.2e-4,
xmin=np.min(clutimes[mask[1]]),
xmax=np.max(clutimes[mask[1]]),
lw=5,
color='#e41a1c',
alpha=.5) #plot significance timepoints for difference effect
for mask in masks_corr:
ax.hlines(
y=-3.4e-4,
xmin=np.min(clutimes[mask[1]]),
xmax=np.max(clutimes[mask[1]]),
lw=5,
color='#4daf4a',
alpha=.5) #plot significance timepoints for difference effect
# fig.savefig(fname = op.join(figpath, 'error_x_diffwave_20subs_channel_'+channel+'_'+ stat +'.eps'), format = 'eps', dpi = 300)
# fig.savefig(fname = op.join(figpath, 'error_x_diffwave_20subs_channel_'+channel+'_'+ stat +'.pdf'), format = 'pdf', dpi = 300)
#
# plt.close()
#%%
plotvmin = dict()
plotvmin['beta'] = -3e-6
plotvmin['tstat'] = -6
gave = mne.grand_average(dat2use['errorcorrect'])
if stat == 'tstat':
gave.data = toverparam(dat2use['errorcorrect'])
gave.plot_joint(topomap_args=dict(outlines='head', extrapolate='head'),
times=np.arange(.1, .7, .1))
topomap_args=dict(outlines='head',
contours=0))
flipped_gave_lvsr_norm.data = np.subtract(gave_lvsr_norm.data,
flipped_gave_lvsr_norm.data)
flipped_gave_lvsr_norm.plot_joint(timefreqs=timefreqs_alpha,
topomap_args=dict(outlines='head',
contours=0))
#for example looking across all channels at motor selection
#really just f*****g around at this point because not sure quite how to get it to work,
gave_gmean = mne.grand_average(alldata_gmean)
flipped_gmean = mne.grand_average(alldata_gmean)
gave_gmean.data = toverparam(alldata_gmean)
flipped_gmean.data = toverparam(alldata_gmean)
renaming_mapping = dict()
for i in range(len(chnames)):
renaming_mapping[chnames[i]] = flippednames[i]
#mne.rename_channels(flipped_gmean.info, renaming_mapping)
import copy
cvsi = copy.deepcopy(gave_gmean)
flipped_gmean.reorder_channels(flippednames)
cvsi.data = np.subtract(gave_gmean.data, flipped_gmean.data)
cvsi.drop_channels(['RM'])
cvsi.plot(picks=['C4'], baseline=(
None, None
)) #C4 is contra to the left hand, which presses space bar to start response
cvsi.plot_joint(baseline=(-0.5, -0.3),
plot_sub = True
for isub in range(subs.size):
if plot_sub:
#fig = data_t['clvsr'][isub].plot_joint(topomap_args = dict(outlines='skirt', contours = 0, vmin=-2,vmax=2),timefreqs = timefreqs_alpha)
data_t['clvsr'][isub].plot_joint(topomap_args=dict(outlines='skirt',
contours=0,
vmin=-2,
vmax=2),
timefreqs=timefreqs_alpha,
title='sub %02d' % (subs[isub]))
# data['clvsr'][isub].plot_joint(topomap_args = dict(outlines='skirt', contours = 0),timefreqs = timefreqs_alpha, title = 'sub %02d'%(subs[isub]))
#%%
contrast = 'cuedvsneut'
gave = mne.grand_average(data_t[contrast])
gave.data = toverparam(data_t[contrast])
gave.plot_joint(
topomap_args=dict(
outlines='skirt', contours=0
), #baseline = (-1.75,-1.25), title ='baselined',#vmin=-4,vmax=4),
timefreqs=timefreqs_alpha) #, vmin=-3, vmax=3)
flip_plot = False
if flip_plot:
chnames = np.array([
'FP1', 'FPZ', 'FP2', 'AF7', 'AF3', 'AFZ', 'AF4', 'AF8', 'F7', 'F5',
'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1',
'FCZ', 'FC2', 'FC4', 'FC6', 'FT8', 'T7', 'C5', 'C3', 'C1', 'CZ', 'C2',
'C4', 'C6', 'T8', 'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4',
'CP6', 'TP8', 'P7', 'P5', 'P3', 'P1', 'PZ', 'P2', 'P4', 'P6', 'P8',
'PO7', 'PO3', 'POZ', 'PO4', 'PO8', 'O1', 'OZ', 'O2'
52,
53,
54,
55,
57,
58,
59,
61,
])
chans_no_midline = np.subtract(chans_no_midline, 1)
chnames_no_midline = np.array(alldata_grandmean[0].ch_names)[chans_no_midline]
#%%
alldata_cuedlvsr_t.pop(6)
gave_clvsr = mne.grand_average(alldata_cuedlvsr_t)
gave_clvsr.data = toverparam(alldata_cuedlvsr_t)
times, freqs = gave_clvsr.times, gave_clvsr.freqs
fig = plt.figure()
ax = fig.subplots(2, 1)
ax[0].contourf(times,
freqs,
np.nanmean(
deepcopy(gave_clvsr).pick_channels(visrightchans).data, 0),
levels=100,
cmap='RdBu_r',
vmin=-2,
vmax=2,
antialiased=False)
ax[1].contourf(times,
(-0.8, 10):(.4, 4),
(-0.6, 10):(.4, 4)}
visleftchans = ['PO3', 'PO7', 'O1']
visrightchans = ['PO4','PO8','O2']
motrightchans = ['C2', 'C4'] #channels contra to the left hand (space bar)
motleftchans = ['C1', 'C3'] #channels contra to the right hand (mouse)
topoargs = dict(outlines= 'head', contours = 0)
topoargs_t = dict(outlines = 'head', contours = 0, vmin=-2, vmax = 2)
#%%
gave_cleft = mne.grand_average(data_baselined_t['cuedleft'])
gave_cleft.data = toverparam(data_baselined_t['cuedleft']); gave_cleft.drop_channels(['RM'])
gave_cleft.plot_joint(topomap_args = dict(outlines='head', contours=0, vmin=-4, vmax=4),
timefreqs = timefreqs_cue, title = 'cued left '+ model+ ' t over tstats')
cleft_vrchans = np.subtract(np.nanmean(deepcopy(gave_cleft).pick_channels(visrightchans).data,0),0) #np.nanmean(deepcopy(gave_cleft).pick_channels(visleftchans).data,0))
times, freqs = deepcopy(gave_cleft).crop(tmin=-2, tmax=0).times, gave_cleft.freqs
-
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.pcolormesh(gave_dtlvsr.times, gave_dtlvsr.freqs, cvsi_dtlvsr, cmap = 'RdBu_r', antialiased = False, vmin = -2, vmax = 2)
ax.imshow(cleft_vrchans, cmap = 'RdBu_r', aspect = 'auto', vmin = -2, vmax = 2, interpolation = 'none', origin = 'lower', extent = (np.min(times), np.max(times), np.min(freqs), np.max(freqs)))
ax.vlines([0, -1.5], linestyle = 'dashed', lw = .75, ymin=1, ymax = 39)
ax.set_ylim(1,39)
ax.set_title('contra-ipsi to cued left')
dat2use = deepcopy(data)
else:
dat2use = deepcopy(data_t)
else:
if stat == 'beta':
dat2use = deepcopy(data_baselined)
else:
dat2use = deepcopy(data_baselined_t)
gave = mne.grand_average(dat2use[contrast])
plot_t=False
if plot_t:
stat = 'tstat'
gave.data = toverparam(dat2use[contrast])
gave.plot_joint(timefreqs = timefreqs_alpha, vmin=topovmin[stat], vmax = topovmin[stat]*-1,
topomap_args = dict(contours = 0,
vmin = topovmin[stat],
vmax = topovmin[stat]*-1))
#%%
#contrast = 'err_pright_neutral'
stat = 'tstat'
if 'vs' in contrast:
if stat == 'beta':
dat2use = deepcopy(data)
else:
dat2use = deepcopy(data_t)
else:
(.9, 10): (.4, 4)
}
visleftchans = ['PO3', 'PO7', 'O1']
visrightchans = ['PO4', 'PO8', 'O2']
motrightchans = ['C2', 'C4'] #channels contra to the left hand (space bar)
motleftchans = ['C1', 'C3'] #channels contra to the right hand (mouse)
topoargs = dict(outlines='head', contours=0)
topoargs_t = dict(outlines='head', contours=0, vmin=-2, vmax=2)
#%%
gave_gmean = mne.grand_average(data_baselined_t['grandmean'])
gave_gmean.data = toverparam(data_baselined_t['grandmean'])
gave_gmean.drop_channels(['RM'])
#gave_gmean.plot_joint(title = 'grandmean, t over tstats', timefreqs = timefreqs_cue,
# topomap_args = dict(outlines = 'head', contours = 0, vmin = -5, vmax = 5))
times = gave_gmean.times
timesrel2cue = np.add(times, 1.5) #this sets zero to be the cue onset time
allfreqs = gave_gmean.freqs
#%%
for i in data_baselined_t['cued']:
i.times = timesrel2cue
gave_cued = mne.grand_average(data_baselined_t['cued'])
gave_cued.data = toverparam(data_baselined_t['cued'])
gave_cued.drop_channels(['RM'])
for isub in range(subs.size):
if plot_sub:
#fig = data_t['clvsr'][isub].plot_joint(topomap_args = dict(outlines='skirt', contours = 0, vmin=-2,vmax=2),timefreqs = timefreqs_alpha)
data_t['clvsr'][isub].plot_joint(topomap_args=dict(outlines='skirt',
contours=0,
vmin=-2,
vmax=2),
timefreqs=timefreqs_alpha,
title='sub %02d' % (subs[isub]))
# data['clvsr'][isub].plot_joint(topomap_args = dict(outlines='skirt', contours = 0),timefreqs = timefreqs_alpha, title = 'sub %02d'%(subs[isub]))
#%%
vmin = -4
vmax = -1 * vmin
contrast = 'clvsr_belowvsabove'
gave = mne.grand_average(data_t[contrast])
gave.data = toverparam(data_t[contrast])
gave.plot_joint(
topomap_args=dict(
outlines='skirt', contours=0, vmin=vmin, vmax=vmax
), #,vmin=-3,vmax=3), #baseline = (-1.75,-1.25), title ='baselined',#vmin=-4,vmax=4),
timefreqs=timefreqs_alpha,
vmin=vmin,
vmax=vmax) #, vmin=-3, vmax=3)
plot_c3 = False
if plot_c3:
c3 = np.squeeze(deepcopy(gave).pick_channels(['C3']).data)
fig, ax = plt.subplots()
ax.imshow(c3,
cmap='RdBu_r',
aspect='auto',
(.9, 10): (.4, 4) } visleftchans = ['PO3', 'PO7', 'O1'] visrightchans = ['PO4', 'PO8', 'O2'] motrightchans = ['C2', 'C4'] #channels contra to the left hand (space bar) motleftchans = ['C1', 'C3'] #channels contra to the right hand (mouse) topoargs = dict(outlines='head', contours=0) topoargs_t = dict(outlines='head', contours=0, vmin=-2, vmax=2) #%% gave_gmean = mne.grand_average(data_baselined_t['grandmean']) gave_gmean.data = toverparam(data_baselined_t['grandmean']) gave_gmean.drop_channels(['RM']) #gave_gmean.plot_joint(title = 'grandmean, t over tstats', timefreqs = timefreqs_cue, # topomap_args = dict(outlines = 'head', contours = 0, vmin = -5, vmax = 5)) times = gave_gmean.times timesrel2cue = np.add(times, 1.5) #this sets zero to be the cue onset time allfreqs = gave_gmean.freqs #%% #for i in data_baselined_t['pleft_cued']: # i.times = timesrel2cue gave_pleft_cued = mne.grand_average(data_baselined_t['pleft_cued']) gave_pleft_cued.data = toverparam(data_baselined_t['pleft_cued']) gave_pleft_cued.drop_channels(['RM'])
else:
baselined = False
if stat == 'beta' and not baselined:
dat2use = deepcopy(data[contrast])
elif stat == 'beta' and baselined:
dat2use = deepcopy(data_baselined[contrast])
elif stat == 'tstat' and not baselined:
dat2use = deepcopy(data_t[contrast])
elif stat == 'tstat' and baselined:
dat2use = deepcopy(data_baselined_t[contrast])
gave = mne.grand_average(dat2use)
if stat == 'tstat':
gave.data = toverparam(dat2use)
#if you're using betas, remember that the scale is still in the same scale as the initial data input (e.g. the values of time frequency)
if stat != 'tstat':
vmin = -2e-11
vmax = np.multiply(vmin, -1)
elif stat == 'tstat':
vmin = -5
vmax = np.multiply(vmin, -1)
#get the subject wise difference between right and left visual channels
lvsrdata = np.empty(shape=(subs.size, allfreqs.size, alltimes.size))
for i in range(subs.size):
lvsrdata[i, :, :] = np.subtract(
np.nanmean(deepcopy(dat2use[i]).pick_channels(visrightchans).data,
axis=0),
lw=5, color = '#4292c6', alpha = .5) #plot significance timepoints for difference effect
#this will plot these significant times as an overlay onto the plot_joint image
#requires finding the right axis within the subplot thats drawn in order to draw them on properly
fig1 = gave.plot_joint(picks = 'eeg', topomap_args = dict(contours=0, outlines='head', vmin = -4, vmax = 4),
times = np.arange(0.1, 0.7, .1))
ax1 = fig1.axes[0]
for mask in range(len(masks_ern)):
ax1.hlines(y = -3.5,
xmin = np.min(clutimes[masks_ern[mask][1]]),
xmax = np.max(clutimes[masks_ern[mask][1]]),
lw=5, color = '#4292c6', alpha = .5) #plot significance timepoints for difference effect
#%%
gave_errorcorr = mne.grand_average(data['errorcorrect']); gave_errorcorr.data = toverparam(data['errorcorrect'])
gave_errorcorr.plot_joint(picks = 'eeg', topomap_args = dict(outlines = 'head', contours = 0), times = np.arange(0.1,0.7,0.05), ts_args=dict(scalings = dict(eeg = 1)),
title = 'error regressor correct trials')
gave_errorincorr = mne.grand_average(data['errorincorrect']); gave_errorincorr.data = toverparam(data['errorincorrect'])
gave_errorincorr.plot_joint(picks = 'eeg', topomap_args = dict(outlines = 'head', contours = 0), times = np.arange(0.1,0.7,0.05), ts_args=dict(scalings = dict(eeg = 1)),
title = 'error regressor incorrect trials')
gave_errorivsc = mne.grand_average(data['errorincorrvscorr']); gave_errorivsc.data = toverparam(data['errorincorrvscorr'])
gave_errorivsc.plot_joint(picks = 'eeg', topomap_args = dict(outlines = 'head', contours = 0), times = np.arange(0.1,0.7,0.05), ts_args=dict(scalings = dict(eeg = 1)),
title = 'error regressor incorrect vs correct trials')
#%%
gave_confcorr = mne.grand_average(data['confcorrect']); gave_confcorr.data = toverparam(data['confcorrect'])
(.8, 22): (.4, 14)
}
timefreqs_theta = {(.4, 6): (.4, 3), (.6, 6): (.4, 3), (.8, 6): (.4, 3)}
visleftchans = ['PO3', 'PO7', 'O1']
visrightchans = ['PO4', 'PO8', 'O2']
motrightchans = ['C2', 'C4'] #channels contra to the left hand (space bar)
motleftchans = ['C1', 'C3'] #channels contra to the right hand (mouse)
frontal_chans = ['CZ', 'FCZ', 'FZ', 'FC1', 'FC2']
ftheta_chans = ['FPZ', 'AFZ', 'FZ', 'AF3', 'AF4']
topoargs = dict(outlines='head', contours=0)
topoargs_t = dict(outlines='head', contours=0, vmin=-2, vmax=2)
#%%
gave_gmean = mne.grand_average(data['grandmean'])
gave_gmean.data = toverparam(data['grandmean'])
#gave_gmean.plot_joint(title = 'grandmean, t over tstats', timefreqs = timefreqs_cue,
# topomap_args = dict(outlines = 'head', contours = 0, vmin = -5, vmax = 5))
times = gave_gmean.times
allfreqs = gave_gmean.freqs
del (gave_gmean)
#%%
#is there anything lateralised in the visual response, relative to the location of the probed item?
#gave_pside = mne.grand_average(data['pside']); gave_pside.data = toverparam(data['pside'])
#gave_pside.plot_joint(title = 'probed item left vs right', timefreqs = timefreqs, topomap_args = topoargs_t, picks = 'eeg')
#%%
#firstly lets just visualise the ERN if we can: this is incorr vs corr
#this is a contrast so we can take the non-baselined data
for contrast in ['grandmean', 'update']:
if 'vs' in contrast:
baselined = False
if stat == 'beta' and not baselined:
dat2use = deepcopy(data)
elif stat == 'beta' and baselined:
dat2use = deepcopy(data_baselined)
elif stat == 'tstat' and not baselined:
dat2use = deepcopy(data_t)
elif stat == 'tstat' and baselined:
dat2use = deepcopy(data_baselined_t)
gave = mne.grand_average(dat2use[contrast])
if plot_t:
gave.data = toverparam(dat2use[contrast])
gave.plot_joint(topomap_args=dict(contours=0,
vmin=vmin['tstat'],
vmax=vmin['tstat'] * -1),
title=contrast,
timefreqs=timefreqs_theta,
vmin=vmin['tstat'],
vmax=vmin['tstat'] * -1)
#channels to focus on for theta: FCz, FC1, FC2, Cz, Fz
for channel in ['FCz', 'Cz', 'Fz']:
gave.plot(picks=channel, vmin=-3, vmax=3)
gave_gmean.plot_joint(
title='all trials response to array, average betas, preglm baseline',
timefreqs=timefreqs,
topomap_args=dict(outlines='head', contours=0))
plot_singlesubs = False
from copy import deepcopy
if plot_singlesubs:
for i in range(len(alldata_grandmean)):
tmp = deepcopy(alldata_grandmean[i])
tmp = tmp.drop_channels(['RM'])
tmp.plot_joint(title='ave response, betas, subject %d' % subs[i],
timefreqs=timefreqs,
topomap_args=dict(outlines='head', contours=0))
gave_gmean = mne.grand_average(alldata_grandmean)
gave_gmean.data = toverparam(alldata_grandmean)
gave_gmean.drop_channels(['RM'])
gave_gmean.plot_joint(
title='all trials response to array, t over betas, preglm baseline',
timefreqs=timefreqs,
topomap_args=dict(outlines='head', contours=0, vmin=-4, vmax=+4))
gave_gmean_t = mne.grand_average(alldata_grandmean_t)
gave_gmean_t.data = toverparam(alldata_grandmean_t)
gave_gmean_t.drop_channels(['RM'])
gave_gmean_t.plot_joint(
title='all trials response to array, t over tstats, preglm baseline',
timefreqs=timefreqs,
topomap_args=dict(outlines='head', contours=0, vmin=-4, vmax=4))
gave_error_t = mne.grand_average(alldata_error_t)
for contrast in ['error',
'conf']: #, 'errorincorrvscorr', 'confincorrvscorr']:
if 'vs' in contrast:
baselined = False
else:
baselined = True
if stat == 'tstat' and baselined:
dat2use = deepcopy(data_baselined_t)
elif stat == 'tstat' and not baselined:
dat2use = deepcopy(data_t)
gave = mne.grand_average(dat2use[contrast])
if plot_t:
gave.data = toverparam(dat2use[contrast])
# gave.plot_joint(timefreqs = timefreqs_alpha, topomap_args = dict(contours = 0, vmin = -2.8, vmax = 2.8), vmin = -2.8, vmax = 2.8, title = contrast)
gave.plot(picks='FCz',
title=contrast + ' at FCz, stat =' + stat,
vmin=-2.8,
vmax=2.8)
#%%
contrast = 'errorcorrect'
channel = ['FCz']
stat = 'tstat'
tmin = 0
tmax = 1.5
(1., 10): (.4, 4)
}
visleftchans = ['PO3', 'PO7', 'O1']
visrightchans = ['PO4', 'PO8', 'O2']
motrightchans = ['C2', 'C4'] #channels contra to the left hand (space bar)
motleftchans = ['C1', 'C3'] #channels contra to the right hand (mouse)
topoargs = dict(outlines='head', contours=0)
topoargs_t = dict(outlines='head', contours=0, vmin=-2, vmax=2)
#%%
gave_clvsr = mne.grand_average(alldata_cuedlvsr_t)
gave_clvsr.data = toverparam(alldata_cuedlvsr_t)
gave_clvsr.drop_channels(['RM'])
gave_clvsr.plot_joint(topomap_args=topoargs_t, timefreqs=timefreqs_alpha)
times, freqs = gave_clvsr.times, gave_clvsr.freqs
cvsn_vis = np.subtract(
np.nanmean(deepcopy(gave_clvsr).pick_channels(visrightchans).data, 0),
np.nanmean(deepcopy(gave_clvsr).pick_channels(visleftchans).data, 0))
cvsn_mouse = np.subtract(
np.nanmean(deepcopy(gave_clvsr).pick_channels(motleftchans).data, 0),
np.nanmean(deepcopy(gave_clvsr).pick_channels(motrightchans).data, 0))
cvsn_space = np.subtract(
np.nanmean(deepcopy(gave_clvsr).pick_channels(motrightchans).data, 0),
np.nanmean(deepcopy(gave_clvsr).pick_channels(motleftchans).data, 0))
(.7, 22): (.2, 16),
(.8, 22): (.2, 16),
(.9, 22): (.2, 16)
}
#going to visualise these regressors now just to look at effects
gave_gmean = mne.grand_average(alldata_grandmean)
gave_gmean.drop_channels(['RM'])
gave_gmean.plot_joint(
title='average feedback evoked response, average betas, preglm baseline',
timefreqs=timefreqs_alpha,
topomap_args=dict(outlines='head', contours=0))
gave_gmean = mne.grand_average(alldata_grandmean)
gave_gmean.data = toverparam(alldata_grandmean)
gave_gmean.drop_channels(['RM'])
gave_gmean.plot_joint(
title='average feedback evoked response, t over betas, preglm baseline',
timefreqs=timefreqs_alpha,
topomap_args=dict(outlines='head', contours=0, vmin=-4, vmax=4))
gave_gmean_t = mne.grand_average(alldata_grandmean_t)
gave_gmean_t.data = toverparam(alldata_grandmean_t)
gave_gmean_t.drop_channels(['RM'])
gave_gmean_t.plot_joint(
title='average feedback evoked response, t over tstats, preglm baselined',
timefreqs=timefreqs_alpha,
topomap_args=dict(outlines='head', contours=0, vmin=-2, vmax=2))
plot_subs = True
visleftchans = ['PO4','PO8','O2']
visrightchans = ['PO3', 'PO7', 'O1']
#%% Question 1 -- Do cues facilitate selection of an item from WM in our task?
#analysis needed:
# -- cued vs neutral (i.e. do visual responses look different in the two?)
# -- cued left vs cued right (do things lateralise in a way that suggests selection)
#just plot the average for all neutral trials first
gave_neutral = mne.grand_average(alldata_neutral_baselined); gave_neutral.drop_channels(['RM'])
gave_neutral.plot_joint(title = 'evoked response to neutral cues, average betas',
timefreqs = timefreqs_alpha,
topomap_args = dict(outlines = 'head', contours = 0))
gave_neutral = mne.grand_average(alldata_neutral_baselined); gave_neutral.data = toverparam(alldata_neutral_baselined);
gave_neutral.drop_channels(['RM'])
gave_neutral.plot_joint(title = 'evoked response to neutral cues, t over betas',
timefreqs = timefreqs_alpha,
topomap_args = dict(outlines = 'head', contours = 0),
vmin = -2, vmax = 2)
gave_neutral_t = mne.grand_average(alldata_neutral_baselined_t); gave_neutral_t.drop_channels(['RM'])
gave_neutral_t.plot_joint(title = 'evoked response to neutral cues, average tstats',
timefreqs = timefreqs_alpha,
topomap_args = dict(outlines = 'head', contours = 0))
gave_neutral_t = mne.grand_average(alldata_neutral_baselined_t); gave_neutral_t.drop_channels(['RM'])
gave_neutral_t.plot_joint(title = 'evoked response to neutral cues, average tstats',
timefreqs = timefreqs_alpha,
topomap_args = dict(outlines = 'head', contours = 0))
44, 43, 42, 41, 40, 39, 38, 37, 36,
53, 52, 51, 50, 49, 48, 47, 46, 45,
58, 57, 56, 55, 54,
61, 60, 59,
62
])
flipids = np.subtract(flipids,1)
flippednames = chnames[flipids]
#for example looking across all channels at motor selection
#really just f*****g around at this point because not sure quite how to get it to work,
gave_gmean = mne.grand_average(alldata_gmean)
flipped_gmean = mne.grand_average(alldata_gmean);
gave_gmean.data = toverparam(alldata_gmean)
flipped_gmean.data = toverparam(alldata_gmean)
renaming_mapping = dict()
for i in range(len(chnames)):
renaming_mapping[chnames[i]] = flippednames[i]
#mne.rename_channels(flipped_gmean.info, renaming_mapping)
import copy
cvsi = copy.deepcopy(gave_gmean)
flipped_gmean.reorder_channels(flippednames)
cvsi.data = np.subtract(gave_gmean.data, flipped_gmean.data)
cvsi.drop_channels(['RM'])
cvsi.plot(picks=['C4'], baseline = (None, None)) #C4 is contra to the left hand, which presses space bar to start response
cvsi.plot_joint(baseline = (-0.5, -0.3), topomap_args = dict(outlines='head', contours=0, vmin=-.5, vmax=.5),
timefreqs = {(0,23):(.4, 14)})
