eeg=False,
stim=True,
eog=True,
exclude='bads')
# Read epochs
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
reject=dict(grad=4000e-13, eog=150e-6))
epochs_list = [epochs[k] for k in event_id]
mne.epochs.equalize_epoch_counts(epochs_list)
data_picks = mne.pick_types(epochs.info, meg=True, exclude='bads')
###############################################################################
# Setup decoding: default is linear SVC
td = TimeDecoding(predict_mode='cross-validation', n_jobs=1)
# Fit
td.fit(epochs)
# Compute accuracy
td.score(epochs)
# Plot scores across time
td.plot(title='Sensor space decoding')
# Temporal decoding
# -----------------
#
# We'll use the default classifer for a binary classification problem
# which is a linear Support Vector Machine (SVM).
td = TimeDecoding(predict_mode='cross-validation', n_jobs=1)
# Fit
td.fit(epochs)
# Compute accuracy
td.score(epochs)
# Plot scores across time
td.plot(title='Sensor space decoding')
###############################################################################
# Generalization Across Time
# --------------------------
#
# Here we'll use a stratified cross-validation scheme.
# make response vector
y = np.zeros(len(epochs.events), dtype=int)
y[epochs.events[:, 2] == 3] = 1
cv = StratifiedKFold(y=y) # do a stratified cross-validation
# define the GeneralizationAcrossTime object
gat = GeneralizationAcrossTime(predict_mode='cross-validation', n_jobs=1,
cv=cv, scorer=roc_auc_score)
# Temporal decoding # ----------------- # # We'll use the default classifer for a binary classification problem # which is a linear Support Vector Machine (SVM). td = TimeDecoding(predict_mode="cross-validation", n_jobs=1) # Fit td.fit(epochs) # Compute accuracy td.score(epochs) # Plot scores across time td.plot(title="Sensor space decoding") ############################################################################### # Generalization Across Time # -------------------------- # # This runs the analysis used in [1]_ and further detailed in [2]_ # # Here we'll use a stratified cross-validation scheme. # make response vector y = np.zeros(len(epochs.events), dtype=int) y[epochs.events[:, 2] == 3] = 1 cv = StratifiedKFold(y=y) # do a stratified cross-validation # define the GeneralizationAcrossTime object
data_picks = mne.pick_types(epochsR.info, meg=True, exclude='bads') epochsR.plot() epochsL.plot() # Temporal decoding ##### Left Hemisphere Temporal Decoding td = TimeDecoding(predict_mode='cross-validation', n_jobs=1) # Fit td.fit(epochsL) # Compute accuracy z = td.score(epochsL) # Plot scores across time td.plot(title='Left Sensor space decoding') ##### Right Hemisphere Temporal Decoding # Fit td.fit(epochsR) # Compute accuracy x = td.score(epochsR) # Plot scores across time td.plot(title='Right Sensor space decoding') ############ epochsL_train = epochsL.copy().crop(tmin=-0.1, tmax=1) labelsL = epochsL.events[:, -1] epochsR_train = epochsR.copy().crop(tmin=-0.1, tmax=1) labelsR = epochsR.events[:, -1]
