

In [ ]:

    
%matplotlib inline

.. _tut_stats_cluster_sensor_2samp_tfr:

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.



In [ ]:

    
# 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 import io
from mne.time_frequency import single_trial_power
from mne.stats import permutation_cluster_test
from mne.datasets import sample

print(__doc__)

Set parameters



In [ ]:

    
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'
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)

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 = raw.info['ch_names'][picks[0]]

# 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)
data_condition_1 = epochs_condition_1.get_data()  # as 3D matrix
data_condition_1 *= 1e13  # change unit to fT / cm

# Load condition 2
event_id = 2
epochs_condition_2 = mne.Epochs(raw, events, event_id, tmin, tmax,
                                picks=picks, baseline=(None, 0),
                                reject=reject)
data_condition_2 = epochs_condition_2.get_data()  # as 3D matrix
data_condition_2 *= 1e13  # change unit to fT / cm

# Take only one channel
data_condition_1 = data_condition_1[:, 97:98, :]
data_condition_2 = data_condition_2[:, 97:98, :]

# Time vector
times = 1e3 * epochs_condition_1.times  # change unit to ms

Factor to downsample the temporal dimension of the PSD computed by single_trial_power. 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.



In [ ]:

    
decim = 2
frequencies = np.arange(7, 30, 3)  # define frequencies of interest
sfreq = raw.info['sfreq']  # sampling in Hz
n_cycles = 1.5

epochs_power_1 = single_trial_power(data_condition_1, sfreq=sfreq,
                                    frequencies=frequencies,
                                    n_cycles=n_cycles, decim=decim)

epochs_power_2 = single_trial_power(data_condition_2, sfreq=sfreq,
                                    frequencies=frequencies,
                                    n_cycles=n_cycles, decim=decim)

epochs_power_1 = epochs_power_1[:, 0, :, :]  # only 1 channel to get 3D matrix
epochs_power_2 = epochs_power_2[:, 0, :, :]  # only 1 channel to get 3D matrix

Compute ratio with baseline power (be sure to correct time vector with decimation factor)



In [ ]:

    
baseline_mask = times[::decim] < 0
epochs_baseline_1 = np.mean(epochs_power_1[:, :, baseline_mask], axis=2)
epochs_power_1 /= epochs_baseline_1[..., np.newaxis]
epochs_baseline_2 = np.mean(epochs_power_2[:, :, baseline_mask], axis=2)
epochs_power_2 /= epochs_baseline_2[..., np.newaxis]

Compute statistic



In [ ]:

    
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



In [ ]:

    
plt.clf()
plt.subplots_adjust(0.12, 0.08, 0.96, 0.94, 0.2, 0.43)
plt.subplot(2, 1, 1)
evoked_contrast = np.mean(data_condition_1, 0) - np.mean(data_condition_2, 0)
plt.plot(times, evoked_contrast.T)
plt.title('Contrast of evoked response (%s)' % ch_name)
plt.xlabel('time (ms)')
plt.ylabel('Magnetic Field (fT/cm)')
plt.xlim(times[0], times[-1])
plt.ylim(-100, 200)

plt.subplot(2, 1, 2)

# 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='RdBu_r')
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)
plt.show()