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# -*- coding: utf-8 -*-
"""
.. _ex-cluster-evoked:
=======================================================
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]
# %%
# 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
# %%
# 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')
# %%
# 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")
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