Permutation F-test on sensor data with 1D cluster level

One tests if the evoked response is significantly different between conditions. Multiple comparison problem is addressed with cluster level permutation test.

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause

import matplotlib.pyplot as plt

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

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'
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)

channel = 'MEG 1332'  # include only this channel in analysis
include = [channel]

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.

Read epochs for the channel of interest

picks = mne.pick_types(raw.info, meg=False, eog=True, include=include,
                       exclude='bads')
event_id = 1
reject = dict(grad=4000e-13, eog=150e-6)
epochs1 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                     baseline=(None, 0), reject=reject)
condition1 = epochs1.get_data()  # as 3D matrix

event_id = 2
epochs2 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                     baseline=(None, 0), reject=reject)
condition2 = epochs2.get_data()  # as 3D matrix

condition1 = condition1[:, 0, :]  # take only one channel to get a 2D array
condition2 = condition2[:, 0, :]  # take only one channel to get a 2D array

Not setting metadata
72 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] sec
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
Not setting metadata
73 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] sec
Applying baseline correction (mode: mean)
4 projection items activated
Loading data for 73 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']
11 bad epochs dropped

Compute statistic

threshold = 6.0
T_obs, clusters, cluster_p_values, H0 = \
    permutation_cluster_test([condition1, condition2], n_permutations=1000,
                             threshold=threshold, tail=1, n_jobs=None,
                             out_type='mask')

stat_fun(H1): min=0.000227 max=38.167093
Running initial clustering …
Found 4 clusters

  0%|          | Permuting : 0/999 [00:00<?,       ?it/s]
  7%|7         | Permuting : 74/999 [00:00<00:00, 2145.08it/s]
 16%|#5        | Permuting : 157/999 [00:00<00:00, 2296.48it/s]
 24%|##3       | Permuting : 238/999 [00:00<00:00, 2326.99it/s]
 32%|###2      | Permuting : 320/999 [00:00<00:00, 2353.61it/s]
 40%|####      | Permuting : 402/999 [00:00<00:00, 2367.88it/s]
 49%|####8     | Permuting : 485/999 [00:00<00:00, 2384.49it/s]
 57%|#####6    | Permuting : 566/999 [00:00<00:00, 2385.47it/s]
 65%|######4   | Permuting : 647/999 [00:00<00:00, 2386.62it/s]
 73%|#######3  | Permuting : 733/999 [00:00<00:00, 2408.05it/s]
 82%|########1 | Permuting : 817/999 [00:00<00:00, 2417.37it/s]
 90%|########9 | Permuting : 895/999 [00:00<00:00, 2402.69it/s]
 98%|#########7| Permuting : 976/999 [00:00<00:00, 2398.13it/s]
100%|##########| Permuting : 999/999 [00:00<00:00, 2413.26it/s]

Plot

times = epochs1.times
fig, (ax, ax2) = plt.subplots(2, 1, figsize=(8, 4))
ax.set_title('Channel : ' + channel)
ax.plot(times, condition1.mean(axis=0) - condition2.mean(axis=0),
        label="ERF Contrast (Event 1 - Event 2)")
ax.set_ylabel("MEG (T / m)")
ax.legend()

for i_c, c in enumerate(clusters):
    c = c[0]
    if cluster_p_values[i_c] <= 0.05:
        h = ax2.axvspan(times[c.start], times[c.stop - 1],
                        color='r', alpha=0.3)
    else:
        ax2.axvspan(times[c.start], times[c.stop - 1], color=(0.3, 0.3, 0.3),
                    alpha=0.3)

hf = plt.plot(times, T_obs, 'g')
ax2.legend((h, ), ('cluster p-value < 0.05', ))
ax2.set_xlabel("time (ms)")
ax2.set_ylabel("f-values")

Estimated memory usage: 9 MB