XDAWN Decoding From EEG data¶

ERP decoding with Xdawn 1 2. For each event type, a set of spatial Xdawn filters are trained and applied on the signal. Channels are concatenated and rescaled to create features vectors that will be fed into a logistic regression.

# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD-3-Clause

import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import MinMaxScaler

from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.decoding import Vectorizer


print(__doc__)

data_path = sample.data_path()

Set parameters and read data

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, tmax = -0.1, 0.3
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
            'Visual/Left': 3, 'Visual/Right': 4}
n_filter = 3

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20, fir_design='firwin')
events = read_events(event_fname)

picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                   exclude='bads')

epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                picks=picks, baseline=None, preload=True,
                verbose=False)

# Create classification pipeline
clf = make_pipeline(Xdawn(n_components=n_filter),
                    Vectorizer(),
                    MinMaxScaler(),
                    LogisticRegression(penalty='l1', solver='liblinear',
                                       multi_class='auto'))

# Get the labels
labels = epochs.events[:, -1]

# Cross validator
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)

# Do cross-validation
preds = np.empty(len(labels))
for train, test in cv.split(epochs, labels):
    clf.fit(epochs[train], labels[train])
    preds[test] = clf.predict(epochs[test])

# Classification report
target_names = ['aud_l', 'aud_r', 'vis_l', 'vis_r']
report = classification_report(labels, preds, target_names=target_names)
print(report)

# Normalized confusion matrix
cm = confusion_matrix(labels, preds)
cm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]

# Plot confusion matrix
fig, ax = plt.subplots(1)
im = ax.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)
ax.set(title='Normalized Confusion matrix')
fig.colorbar(im)
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
fig.tight_layout()
ax.set(ylabel='True label', xlabel='Predicted label')

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.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 20 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 20.00 Hz
- Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 22.50 Hz)
- Filter length: 497 samples (3.310 sec)

Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
Computing rank from data with rank='full'
    EEG: rank 59 from info
    Created an SSP operator (subspace dimension = 1)
Reducing data rank from 59 -> 59
Estimating covariance using EMPIRICAL
Done.
              precision    recall  f1-score   support

       aud_l       0.79      0.69      0.74        72
       aud_r       0.72      0.81      0.76        73
       vis_l       0.99      0.97      0.98        73
       vis_r       0.96      0.97      0.96        70

    accuracy                           0.86       288
   macro avg       0.86      0.86      0.86       288
weighted avg       0.86      0.86      0.86       288

The patterns_ attribute of a fitted Xdawn instance (here from the last cross-validation fold) can be used for visualization.

fig, axes = plt.subplots(nrows=len(event_id), ncols=n_filter,
                         figsize=(n_filter, len(event_id) * 2))
fitted_xdawn = clf.steps[0][1]
info = create_info(epochs.ch_names, 1, epochs.get_channel_types())
info.set_montage(epochs.get_montage())
for ii, cur_class in enumerate(sorted(event_id)):
    cur_patterns = fitted_xdawn.patterns_[cur_class]
    pattern_evoked = EvokedArray(cur_patterns[:n_filter].T, info, tmin=0)
    pattern_evoked.plot_topomap(
        times=np.arange(n_filter),
        time_format='Component %d' if ii == 0 else '', colorbar=False,
        show_names=False, axes=axes[ii], show=False)
    axes[ii, 0].set(ylabel=cur_class)
fig.tight_layout(h_pad=1.0, w_pad=1.0, pad=0.1)

References¶

1: Bertrand Rivet, Antoine Souloumiac, Virginie Attina, and Guillaume Gibert. xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Transactions on Biomedical Engineering, 56(8):2035–2043, 2009. doi:10.1109/TBME.2009.2012869.
2: Bertrand Rivet, Hubert Cecotti, Antoine Souloumiac, Emmanuel Maby, and Jérémie Mattout. Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI. In Proceedings of EUSIPCO-2011, 1382–1386. Barcelona, 2011. IEEE. URL: https://ieeexplore.ieee.org/document/7073970.

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Analysis of evoked response using ICA and PCA reduction techniques

Compute effect-matched-spatial filtering (EMS)