One Way manova time

One way manova to compare Left vs Right in time.

import numpy as np
import seaborn as sns

from time import time
from pylab import plt

from mne import Epochs, pick_types, events_from_annotations
from mne.io import concatenate_raws
from mne.io.edf import read_raw_edf
from mne.datasets import eegbci

from pyriemann.stats import PermutationDistance
from pyriemann.estimation import Covariances

sns.set_style('whitegrid')

Set parameters and read data

# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -2., 6.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14]  # motor imagery: hands vs feet

raw_files = [
    read_raw_edf(f, preload=True, verbose=False)
    for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)

events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))
picks = pick_types(
    raw.info, meg=False, eeg=True, stim=False, eog=False, exclude='bads')

raw.filter(7., 35., method='iir', picks=picks)

epochs = Epochs(
    raw,
    events,
    event_id,
    tmin,
    tmax,
    proj=True,
    picks=picks,
    baseline=None,
    preload=True,
    verbose=False)
labels = epochs.events[:, -1] - 2

# get epochs
epochs_data = epochs.get_data()
Used Annotations descriptions: ['T1', 'T2']
Filtering raw data in 3 contiguous segments
Setting up band-pass filter from 7 - 35 Hz

IIR filter parameters
---------------------
Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:
- Filter order 16 (effective, after forward-backward)
- Cutoffs at 7.00, 35.00 Hz: -6.02, -6.02 dB

Pairwise distance based permutation test

covest = Covariances()

Fs = 160
window = 2 * Fs
Nwindow = 20
Ns = epochs_data.shape[2]
step = int((Ns - window) / Nwindow)
time_bins = range(0, Ns - window, step)

pv = []
Fv = []
# For each frequency bin, estimate the stats
t_init = time()
for t in time_bins:
    covmats = covest.fit_transform(epochs_data[:, ::1, t:(t + window)])
    p_test = PermutationDistance(1000, metric='riemann', mode='pairwise')
    p, F = p_test.test(covmats, labels, verbose=False)
    pv.append(p)
    Fv.append(F[0])
duration = time() - t_init
# plot result
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
sig = 0.05
times = np.array(time_bins) / float(Fs) + tmin

axes.plot(times, Fv, lw=2, c='k')
plt.xlabel('Time (sec)')
plt.ylabel('Score')

a = np.where(np.diff(np.array(pv) < sig))[0]
a = a.reshape(int(len(a) / 2), 2)
st = (times[1] - times[0]) / 2.0
for p in a:
    axes.axvspan(times[p[0]] - st, times[p[1]] + st, facecolor='g', alpha=0.5)
axes.legend(['Score', 'p<%.2f' % sig])
axes.set_title('Pairwise distance - %.1f sec.' % duration)

sns.despine()
plt.tight_layout()
plt.show()
Pairwise distance - 19.6 sec.

Total running time of the script: ( 0 minutes 20.226 seconds)

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