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