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 <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import numpy as np

import mne
from mne import io
from mne.datasets import sample
from mne.stats import permutation_t_test

print(__doc__)

Set parameters

data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
event_fname = meg_path / "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)

# pick MEG Gradiometers
picks = mne.pick_types(
    raw.info, meg="grad", eeg=False, stim=False, eog=True, 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=None)

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)

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Not setting metadata
72 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
4 projection items activated
Loading data for 72 events and 106 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
16 bad epochs dropped
Permuting 49999 times...
Number of significant sensors : 11
Sensors names : ['MEG 0243', 'MEG 1323', 'MEG 1333', 'MEG 1613', 'MEG 1643', 'MEG 2423', 'MEG 2522', 'MEG 2622', 'MEG 2632', 'MEG 2642', 'MEG 2643']

View location of significantly active sensors

evoked = mne.EvokedArray(-np.log10(p_values)[:, np.newaxis], epochs.info, tmin=0.0)

# Extract mask and indices of active sensors in the layout
mask = p_values[:, np.newaxis] <= 0.05

evoked.plot_topomap(
    ch_type="grad",
    times=[0],
    scalings=1,
    time_format=None,
    cmap="Reds",
    vlim=(0.0, np.max),
    units="-log10(p)",
    cbar_fmt="-%0.1f",
    mask=mask,
    size=3,
    show_names=lambda x: x[4:] + " " * 20,
    time_unit="s",
)

Estimated memory usage: 342 MB