""" ================================= Permutation T-test on sensor data ================================= One tests if the signal significantly deviates from 0 during a fixed time window of interest. Here computation is performed on MNE sample dataset between 40 and 60 ms. """ # Authors: Alexandre Gramfort # # License: BSD (3-clause) import numpy as np import mne from mne import io from mne.stats import permutation_t_test from mne.datasets import sample print(__doc__) ############################################################################### # Set parameters data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif' event_id = 1 tmin = -0.2 tmax = 0.5 # Setup for reading the raw data raw = io.read_raw_fif(raw_fname) events = mne.read_events(event_fname) # Set up pick list: MEG + STI 014 - bad channels (modify to your needs) include = [] # or stim channel ['STI 014'] raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more # pick MEG Gradiometers picks = mne.pick_types(raw.info, meg='grad', eeg=False, stim=False, eog=True, include=include, exclude='bads') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6)) data = epochs.get_data() times = epochs.times temporal_mask = np.logical_and(0.04 <= times, times <= 0.06) data = np.mean(data[:, :, temporal_mask], axis=2) n_permutations = 50000 T0, p_values, H0 = permutation_t_test(data, n_permutations, n_jobs=1) significant_sensors = picks[p_values <= 0.05] significant_sensors_names = [raw.ch_names[k] for k in significant_sensors] print("Number of significant sensors : %d" % len(significant_sensors)) print("Sensors names : %s" % significant_sensors_names) ############################################################################### # View location of significantly active sensors evoked = mne.EvokedArray(-np.log10(p_values)[:, np.newaxis], epochs.info, tmin=0.) # Extract mask and indices of active sensors in layout stats_picks = mne.pick_channels(evoked.ch_names, significant_sensors_names) mask = p_values[:, np.newaxis] <= 0.05 evoked.plot_topomap(ch_type='grad', times=[0], scale=1, time_format=None, cmap='Reds', vmin=0., vmax=np.max, unit='-log10(p)', cbar_fmt='-%0.1f', mask=mask, size=3, show_names=lambda x: x[4:] + ' ' * 20)