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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()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/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]
Out:
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
Out:
Not setting metadata
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
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=1,
out_type='mask')
Out:
stat_fun(H1): min=0.000227 max=38.167093
Running initial clustering
Found 4 clusters
Permuting 999 times...
0%| | : 0/999 [00:00<?, ?it/s]
15%|#5 | : 152/999 [00:00<00:00, 4426.18it/s]
32%|###2 | : 321/999 [00:00<00:00, 4711.63it/s]
48%|####8 | : 484/999 [00:00<00:00, 4744.67it/s]
65%|######4 | : 647/999 [00:00<00:00, 4760.78it/s]
81%|########1 | : 810/999 [00:00<00:00, 4773.28it/s]
97%|#########6| : 967/999 [00:00<00:00, 4745.37it/s]
100%|##########| : 999/999 [00:00<00:00, 4729.99it/s]
Computing cluster p-values
Done.
Plot
times = epochs1.times
plt.close('all')
plt.subplot(211)
plt.title('Channel : ' + channel)
plt.plot(times, condition1.mean(axis=0) - condition2.mean(axis=0),
label="ERF Contrast (Event 1 - Event 2)")
plt.ylabel("MEG (T / m)")
plt.legend()
plt.subplot(212)
for i_c, c in enumerate(clusters):
c = c[0]
if cluster_p_values[i_c] <= 0.05:
h = plt.axvspan(times[c.start], times[c.stop - 1],
color='r', alpha=0.3)
else:
plt.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')
plt.legend((h, ), ('cluster p-value < 0.05', ))
plt.xlabel("time (ms)")
plt.ylabel("f-values")
plt.show()
Total running time of the script: ( 0 minutes 6.227 seconds)
Estimated memory usage: 8 MB