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%matplotlib inline

Non-parametric between conditions cluster statistic on single trial power

This script shows how to compare clusters in time-frequency power estimates between conditions. It uses a non-parametric statistical procedure based on permutations and cluster level statistics.

The procedure consists in:

extracting epochs for 2 conditions
compute single trial power estimates
baseline line correct the power estimates (power ratios)
compute stats to see if the power estimates are significantly different between conditions.



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# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_test
from mne.datasets import sample

print(__doc__)

Set parameters



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data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
tmin, tmax = -0.2, 0.5

# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)

include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more

# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
                       stim=False, include=include, exclude='bads')

ch_name = 'MEG 1332'  # restrict example to one channel

# Load condition 1
reject = dict(grad=4000e-13, eog=150e-6)
event_id = 1
epochs_condition_1 = mne.Epochs(raw, events, event_id, tmin, tmax,
                                picks=picks, baseline=(None, 0),
                                reject=reject, preload=True)
epochs_condition_1.pick_channels([ch_name])

# Load condition 2
event_id = 2
epochs_condition_2 = mne.Epochs(raw, events, event_id, tmin, tmax,
                                picks=picks, baseline=(None, 0),
                                reject=reject, preload=True)
epochs_condition_2.pick_channels([ch_name])

Factor to downsample the temporal dimension of the TFR computed by tfr_morlet. Decimation occurs after frequency decomposition and can be used to reduce memory usage (and possibly comptuational time of downstream operations such as nonparametric statistics) if you don't need high spectrotemporal resolution.



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decim = 2
frequencies = np.arange(7, 30, 3)  # define frequencies of interest
n_cycles = 1.5

tfr_epochs_1 = tfr_morlet(epochs_condition_1, frequencies,
                          n_cycles=n_cycles, decim=decim,
                          return_itc=False, average=False)

tfr_epochs_2 = tfr_morlet(epochs_condition_2, frequencies,
                          n_cycles=n_cycles, decim=decim,
                          return_itc=False, average=False)

tfr_epochs_1.apply_baseline(mode='ratio', baseline=(None, 0))
tfr_epochs_2.apply_baseline(mode='ratio', baseline=(None, 0))

epochs_power_1 = tfr_epochs_1.data[:, 0, :, :]  # only 1 channel as 3D matrix
epochs_power_2 = tfr_epochs_2.data[:, 0, :, :]  # only 1 channel as 3D matrix

Compute statistic



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threshold = 6.0
T_obs, clusters, cluster_p_values, H0 = \
    permutation_cluster_test([epochs_power_1, epochs_power_2],
                             n_permutations=100, threshold=threshold, tail=0)

View time-frequency plots



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times = 1e3 * epochs_condition_1.times  # change unit to ms
evoked_condition_1 = epochs_condition_1.average()
evoked_condition_2 = epochs_condition_2.average()

plt.figure()
plt.subplots_adjust(0.12, 0.08, 0.96, 0.94, 0.2, 0.43)

plt.subplot(2, 1, 1)
# Create new stats image with only significant clusters
T_obs_plot = np.nan * np.ones_like(T_obs)
for c, p_val in zip(clusters, cluster_p_values):
    if p_val <= 0.05:
        T_obs_plot[c] = T_obs[c]

plt.imshow(T_obs,
           extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
           aspect='auto', origin='lower', cmap='gray')
plt.imshow(T_obs_plot,
           extent=[times[0], times[-1], frequencies[0], frequencies[-1]],
           aspect='auto', origin='lower', cmap='RdBu_r')

plt.xlabel('Time (ms)')
plt.ylabel('Frequency (Hz)')
plt.title('Induced power (%s)' % ch_name)

ax2 = plt.subplot(2, 1, 2)
evoked_contrast = mne.combine_evoked([evoked_condition_1, evoked_condition_2],
                                     weights=[1, -1])
evoked_contrast.plot(axes=ax2)

plt.show()