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"""
.. _tut-cluster-tfr:
=========================================================================
Non-parametric between conditions cluster statistic on single trial power
=========================================================================
This script shows how to compare clusters in time-frequency
power estimates between conditions. It uses a non-parametric
statistical procedure based on permutations and cluster
level statistics.
The procedure consists of:
- extracting epochs for 2 conditions
- compute single trial power estimates
- baseline line correct the power estimates (power ratios)
- compute stats to see if the power estimates are significantly different
between conditions.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
# %%
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.stats import permutation_cluster_test
print(__doc__)
# %%
# Set parameters
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_raw.fif"
event_fname = meg_path / "sample_audvis_raw-eve.fif"
tmin, tmax = -0.2, 0.5
# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)
include = []
raw.info["bads"] += ["MEG 2443", "EEG 053"] # bads + 2 more
# picks MEG gradiometers
picks = mne.pick_types(
raw.info,
meg="grad",
eeg=False,
eog=True,
stim=False,
include=include,
exclude="bads",
)
ch_name = "MEG 1332" # restrict example to one channel
# Load condition 1
reject = dict(grad=4000e-13, eog=150e-6)
event_id = 1
epochs_condition_1 = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject,
preload=True,
)
epochs_condition_1.pick([ch_name])
# Load condition 2
event_id = 2
epochs_condition_2 = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject,
preload=True,
)
epochs_condition_2.pick([ch_name])
# %%
# Factor to downsample the temporal dimension of the TFR computed by
# tfr_morlet. Decimation occurs after frequency decomposition and can
# be used to reduce memory usage (and possibly comptuational time of downstream
# operations such as nonparametric statistics) if you don't need high
# spectrotemporal resolution.
decim = 2
freqs = np.arange(7, 30, 3) # define frequencies of interest
n_cycles = 1.5
tfr_kwargs = dict(
method="morlet",
freqs=freqs,
n_cycles=n_cycles,
decim=decim,
return_itc=False,
average=False,
)
tfr_epochs_1 = epochs_condition_1.compute_tfr(**tfr_kwargs)
tfr_epochs_2 = epochs_condition_2.compute_tfr(**tfr_kwargs)
tfr_epochs_1.apply_baseline(mode="ratio", baseline=(None, 0))
tfr_epochs_2.apply_baseline(mode="ratio", baseline=(None, 0))
epochs_power_1 = tfr_epochs_1.data[:, 0, :, :] # only 1 channel as 3D matrix
epochs_power_2 = tfr_epochs_2.data[:, 0, :, :] # only 1 channel as 3D matrix
# %%
# Compute statistic
# -----------------
threshold = 6.0
F_obs, clusters, cluster_p_values, H0 = permutation_cluster_test(
[epochs_power_1, epochs_power_2],
out_type="mask",
n_permutations=100,
threshold=threshold,
tail=0,
seed=np.random.default_rng(seed=8675309),
)
# %%
# View time-frequency plots
# -------------------------
times = 1e3 * epochs_condition_1.times # change unit to ms
fig, (ax, ax2) = plt.subplots(2, 1, figsize=(6, 4), layout="constrained")
# Compute the difference in evoked to determine which was greater since
# we used a 1-way ANOVA which tested for a difference in population means
evoked_power_1 = epochs_power_1.mean(axis=0)
evoked_power_2 = epochs_power_2.mean(axis=0)
evoked_power_contrast = evoked_power_1 - evoked_power_2
signs = np.sign(evoked_power_contrast)
# Create new stats image with only significant clusters
F_obs_plot = np.nan * np.ones_like(F_obs)
for c, p_val in zip(clusters, cluster_p_values):
if p_val <= 0.05:
F_obs_plot[c] = F_obs[c] * signs[c]
ax.imshow(
F_obs,
extent=[times[0], times[-1], freqs[0], freqs[-1]],
aspect="auto",
origin="lower",
cmap="gray",
)
max_F = np.nanmax(abs(F_obs_plot))
ax.imshow(
F_obs_plot,
extent=[times[0], times[-1], freqs[0], freqs[-1]],
aspect="auto",
origin="lower",
cmap="RdBu_r",
vmin=-max_F,
vmax=max_F,
)
ax.set_xlabel("Time (ms)")
ax.set_ylabel("Frequency (Hz)")
ax.set_title(f"Induced power ({ch_name})")
# plot evoked
evoked_condition_1 = epochs_condition_1.average()
evoked_condition_2 = epochs_condition_2.average()
evoked_contrast = mne.combine_evoked(
[evoked_condition_1, evoked_condition_2], weights=[1, -1]
)
evoked_contrast.plot(axes=ax2, time_unit="s")
|
