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"""
============================
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.
References
----------
.. [1] Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN
algorithm to enhance evoked potentials: application to brain-computer
interface. Biomedical Engineering, IEEE Transactions on, 56(8),
2035-2043.
.. [2] Rivet, B., Cecotti, H., Souloumiac, A., Maby, E., & Mattout, J. (2011,
August). Theoretical analysis of xDAWN algorithm: application to an
efficient sensor selection in a P300 BCI. In Signal Processing
Conference, 2011 19th European (pp. 1382-1386). IEEE.
"""
# 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
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.decoding import Vectorizer
from mne.viz import tight_layout
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 = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)
# 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=3),
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
plt.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Normalized Confusion matrix')
plt.colorbar()
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
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