mne.viz.plot_epochs_image(epochs['imag/face'], picks=channel_ind)
# plot 95% CI :
# -
plt.plot()
diff_evoked.plot(axes=ax[0,0])
diff_evoked.plot()
diff=np.sum(diff_evoked.data ** 2, axis=0)
diff = mne.baseline.rescale(diff, times, baseline=(None, 0))
ax[0,0].set_title('%s' %subject)
times= diff_evoked.times * 1e3
ci_low, ci_up = _bootstrap_ci(diff_evoked.data, random_state=0,
stat_fun=median)
ci_low = mne.baseline.rescale(ci_low, diff_evoked.times, baseline=(None, 0))
ci_up = mne.baseline.rescale(ci_up, diff_evoked.times, baseline=(None, 0))
ax[0,0].fill_between(times, diff + ci_up, diff - ci_low, color='red', alpha=0.3)
# plot stim face menus stim house
#mne.combine_evoked([stim_face,-stim_house], weights= 'equal').plot_joint(title='%s ERP diff Stim Face minus Stim House'%subject )
#plot imag face menus imag house
#mne.combine_evoked([imag_face,-imag_house], weights= 'equal').plot_joint(title='%s ERP diff Imag Face minus Imag House'%subject)
info=epochs.info,
tmin=epochs.tmin)
# now average and move on
frequency_map.append(((band, fmin, fmax), epochs.average()))
fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)
colors = plt.get_cmap('winter_r')(np.linspace(0, 1, 4))
for ((freq_name, fmin, fmax),
average), color, ax in zip(frequency_map, colors,
axes.ravel()[::-1]):
times = average.times * 1e3
gfp = np.sum(average.data**2, axis=0)
gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))
ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)
ax.axhline(0, linestyle='--', color='grey', linewidth=2)
ci_low, ci_up = _bootstrap_ci(average.data,
random_state=0,
stat_fun=lambda x: np.sum(x**2, axis=0))
ci_low = rescale(ci_low, average.times, baseline=(None, 0))
ci_up = rescale(ci_up, average.times, baseline=(None, 0))
ax.fill_between(times, gfp + ci_up, gfp - ci_low, color=color, alpha=0.3)
ax.grid(True)
ax.set_ylabel('GFP')
ax.annotate('%s (%d-%dHz)' % (freq_name, fmin, fmax),
xy=(0.95, 0.8),
horizontalalignment='right',
xycoords='axes fraction')
ax.set_xlim(-1000, 3000)
axes.ravel()[-1].set_xlabel('Time [ms]')
###############################################################################
# Now we can compute the Global Field Power
# We can track the emergence of spatial patterns compared to baseline
# for each frequency band, with a bootstrapped confidence interval.
#
# We see dominant responses in the Alpha and Beta bands.
fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)
colors = plt.get_cmap('winter_r')(np.linspace(0, 1, 4))
for ((freq_name, fmin, fmax), average), color, ax in zip(
frequency_map, colors, axes.ravel()[::-1]):
times = average.times * 1e3
gfp = np.sum(average.data ** 2, axis=0)
gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))
ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)
ax.axhline(0, linestyle='--', color='grey', linewidth=2)
ci_low, ci_up = _bootstrap_ci(average.data, random_state=0,
statfun=lambda x: np.sum(x ** 2, axis=0))
ci_low = rescale(ci_low, average.times, baseline=(None, 0))
ci_up = rescale(ci_up, average.times, baseline=(None, 0))
ax.fill_between(times, gfp + ci_up, gfp - ci_low, color=color, alpha=0.3)
ax.grid(True)
ax.set_ylabel('GFP')
ax.annotate('%s (%d-%dHz)' % (freq_name, fmin, fmax),
xy=(0.95, 0.8),
horizontalalignment='right',
xycoords='axes fraction')
ax.set_xlim(-1000, 3000)
axes.ravel()[-1].set_xlabel('Time [ms]')