baseline=(-1, 0),
reject={'eeg': rej_thresh},
preload=True,
verbose=False,
picks=[0, 1, 2, 3])
print('sample drop %: ', (1 - len(epochs.events) / len(events)) * 100)
conditions = OrderedDict()
conditions['LeftCue'] = [1]
conditions['RightCue'] = [2]
fig, ax = plot_conditions(epochs,
conditions=conditions,
ci=97.5,
n_boot=1000,
title='',
diff_waveform=(1, 2),
ylim=(-20, 20))
###################################################################################################
# Spectrogram
# -----------------------------
#
# We can also look for SSVEPs in the spectrogram, which uses color to represent the power of frequencies in the EEG signal over time
#
frequencies = np.linspace(6, 30, 100, endpoint=True)
wave_cycles = 6
reject={'eeg': 5e-5},
preload=True,
verbose=False,
picks=[0, 1, 2, 3])
print('sample drop %: ', (1 - len(epochs.events) / len(events)) * 100)
epochs
###################################################################################################
# Epoch average
# ----------------------------
conditions = OrderedDict()
conditions['House'] = [1]
conditions['Face'] = [2]
fig, ax = plot_conditions(
epochs,
conditions=conditions,
ci=97.5,
n_boot=1000,
title='',
diff_waveform=None, #(1, 2))
channel_order=[1, 0, 2, 3]
) # reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes
# Manually adjust the ylims
for i in [0, 2]:
ax[i].set_ylim([-0.5, 0.5])
for i in [1, 3]:
ax[i].set_ylim([-1.5, 2.5])