drawer = Drawer()
events_ID = ['1', '2', '4']
n_components = 6
n_jobs = 48
# %%
for name in ['MEG_S02', 'EEG_S02']:
# -----------------------------------------------
# Init data manager
dm = DataManager(name)
# Load epochs
dm.load_epochs(recompute=False)
# -----------------------------------------------
# Separate epochs
epochs_1, epochs_2 = dm.leave_one_session_out(includes=[1, 3, 5],
excludes=[2, 4, 6])
# Xdawn enhancemen
xdawn = mne.preprocessing.Xdawn(n_components=n_components)
# Fit
xdawn.fit(epochs_1)
# Baseline correction
epochs_1.apply_baseline((None, 0))
epochs_2.apply_baseline((None, 0))
epochs_1 = epochs_1[events_ID]
epochs_2 = epochs_2[events_ID]
# Apply using xdawn
epochs_1_xdawn = xdawn.apply(epochs_1)
drawer = Drawer()
events_ID = ['1', '2', '4']
n_jobs = 48
# %%
name = 'MEG_S02'
# for name in [f'MEG_S{j+1:02d}' for j in range(2, 3)]:
# -----------------------------------------------
# Init data manager
dm = DataManager(name)
# Load epochs
dm.load_epochs(recompute=False)
# -----------------------------------------------
# Separate epochs
epochs, epochs_2 = dm.leave_one_session_out(includes=[1, 2, 3, 4, 5, 6, 7],
excludes=[0])
epochs = epochs[events_ID]
# Xdawn enhancemen
xdawn = mne.preprocessing.Xdawn(n_components=12)
# Fit
xdawn.fit(epochs)
# Baseline correction
epochs.apply_baseline((None, 0))
epochs = epochs[events_ID]
# -----------------------------------------------
data = np.mean(xdawn.transform(epochs['1']), axis=0)
data2 = np.mean(xdawn.transform(epochs['2']), axis=0)
times = epochs.times