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
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import StratifiedKFold
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler

from mne import Epochs, io, pick_types, read_events
from mne.datasets import sample
from mne.decoding import Vectorizer, XdawnTransformer, get_spatial_filter_from_estimator

print(__doc__)

data_path = sample.data_path()

Set parameters and read data

meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
event_fname = meg_path / "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(
    XdawnTransformer(n_components=n_filter),
    Vectorizer(),
    MinMaxScaler(),
    OneVsRestClassifier(LogisticRegression(penalty="l1", solver="liblinear")),
)

# Get the data and labels
# X is of shape (n_epochs, n_channels, n_times)
X = epochs.get_data(copy=False)
y = epochs.events[:, -1]

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

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

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

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

# Plot confusion matrix
fig, ax = plt.subplots(1, layout="constrained")
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)
ax.set(ylabel="True label", xlabel="Predicted label")

Patterns of a fitted XdawnTransformer instance (here from the last cross-validation fold) can be visualized using SpatialFilter container.

# Instantiate SpatialFilter
spf = get_spatial_filter_from_estimator(
    clf, info=epochs.info, step_name="xdawntransformer"
)

# Let's first examine the scree plot of generalized eigenvalues
# for each class.
spf.plot_scree(title="")

# We can see that for all four classes ~five largest components
# capture most of the variance, let's plot their patterns.
# Each class will now return its own figure
components_to_plot = np.arange(5)
figs = spf.plot_patterns(
    # Indices of patterns to plot,
    # we will plot the first three for each class
    components=components_to_plot,
    show=False,  # to set the titles below
)

# Set the class titles
event_id_reversed = {v: k for k, v in event_id.items()}
for fig, class_idx in zip(figs, clf[0].classes_):
    class_name = event_id_reversed[class_idx]
    fig.suptitle(class_name, fontsize=16)

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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