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] 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
Not setting metadata
73 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
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]
 11%|#1        | Permuting : 110/999 [00:00<00:00, 3195.02it/s]
 21%|##1       | Permuting : 212/999 [00:00<00:00, 3100.73it/s]
 31%|###       | Permuting : 307/999 [00:00<00:00, 2998.90it/s]
 40%|####      | Permuting : 404/999 [00:00<00:00, 2956.37it/s]
 52%|#####2    | Permuting : 523/999 [00:00<00:00, 3075.86it/s]
 65%|######5   | Permuting : 651/999 [00:00<00:00, 3208.05it/s]
 78%|#######8  | Permuting : 781/999 [00:00<00:00, 3310.64it/s]
 93%|#########2| Permuting : 927/999 [00:00<00:00, 3453.51it/s]
100%|##########| Permuting : 999/999 [00:00<00:00, 3504.21it/s]
100%|##########| Permuting : 999/999 [00:00<00:00, 3443.24it/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: 10 MB