Example TFCE stats for GAT

TFCE_stats_gat.py

import matplotlib.pyplot as plt
from mne.stats import spatio_temporal_cluster_1samp_test

# Gather all scores
scores = np.array([gat.scores_ for gat in gat_list])
gat_mean = copy.deepcopy(gat_list[0])
gat_mean.scores_ = np.mean(scores, axis=0)

# STATS
chance = 0.5  # chance level; if it's an AUC, it has to be .5
alpha = 0.05

T_obs_, clusters, p_values, _ = spatio_temporal_cluster_1samp_test(
    scores - chance, out_type='mask', n_permutations=128,
    threshold=dict(start=2, step=2.), n_jobs=-1)

p_values = p_values.reshape(scores.shape[1:])

# PLOT
fig = gat_mean.plot(show=False)
ax = fig.axes[0]
xx, yy = np.meshgrid(gat_mean.train_times_['times'],
                     gat_mean.test_times_['times'][0],
                     copy=False, indexing='xy')
ax.contour(xx, yy, p_values < alpha, colors='black', levels=[0])
plt.show()