Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Decoding of motor imagery applied to EEG data decomposed using CSP. A classifier is then applied to features extracted on CSP-filtered signals.

See https://en.wikipedia.org/wiki/Common_spatial_pattern and [1]. The EEGBCI dataset is documented in [2] and on the PhysioNet documentation page. The dataset is available at PhysioNet [3].

# Authors: Martin Billinger <martin.billinger@tugraz.at>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import ShuffleSplit, cross_val_score
from sklearn.pipeline import Pipeline

from mne import Epochs, pick_types
from mne.channels import make_standard_montage
from mne.datasets import eegbci
from mne.decoding import CSP
from mne.io import concatenate_raws, read_raw_edf

print(__doc__)

# #############################################################################
# # Set parameters and read data

# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1.0, 4.0
subjects = 1
runs = [6, 10, 14]  # motor imagery: hands vs feet

raw_fnames = eegbci.load_data(subjects, runs)
raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames])
eegbci.standardize(raw)  # set channel names
montage = make_standard_montage("standard_1005")
raw.set_montage(montage)
raw.annotations.rename(dict(T1="hands", T2="feet"))  # as documented on PhysioNet
raw.set_eeg_reference(projection=True)

# Apply band-pass filter
raw.filter(7.0, 30.0, fir_design="firwin", skip_by_annotation="edge")

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

# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(
    raw,
    event_id=["hands", "feet"],
    tmin=tmin,
    tmax=tmax,
    proj=True,
    picks=picks,
    baseline=None,
    preload=True,
)
epochs_train = epochs.copy().crop(tmin=1.0, tmax=2.0)
labels = epochs.events[:, -1] - 2

Classification with linear discrimant analysis

# Define a monte-carlo cross-validation generator (reduce variance):
scores = []
epochs_data = epochs.get_data(copy=False)
epochs_data_train = epochs_train.get_data(copy=False)
cv = ShuffleSplit(10, test_size=0.2, random_state=42)
cv_split = cv.split(epochs_data_train)

# Assemble a classifier
lda = LinearDiscriminantAnalysis()
csp = CSP(n_components=4, reg=None, log=True, norm_trace=False)

# Use scikit-learn Pipeline with cross_val_score function
clf = Pipeline([("CSP", csp), ("LDA", lda)])
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=None)

# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1.0 - class_balance)
print(f"Classification accuracy: {np.mean(scores)} / Chance level: {class_balance}")

# plot CSP patterns estimated on full data for visualization
csp.fit_transform(epochs_data, labels)

csp.plot_patterns(epochs.info, ch_type="eeg", units="Patterns (AU)", size=1.5)

Look at performance over time

sfreq = raw.info["sfreq"]
w_length = int(sfreq * 0.5)  # running classifier: window length
w_step = int(sfreq * 0.1)  # running classifier: window step size
w_start = np.arange(0, epochs_data.shape[2] - w_length, w_step)

scores_windows = []

for train_idx, test_idx in cv_split:
    y_train, y_test = labels[train_idx], labels[test_idx]

    X_train = csp.fit_transform(epochs_data_train[train_idx], y_train)
    X_test = csp.transform(epochs_data_train[test_idx])

    # fit classifier
    lda.fit(X_train, y_train)

    # running classifier: test classifier on sliding window
    score_this_window = []
    for n in w_start:
        X_test = csp.transform(epochs_data[test_idx][:, :, n : (n + w_length)])
        score_this_window.append(lda.score(X_test, y_test))
    scores_windows.append(score_this_window)

# Plot scores over time
w_times = (w_start + w_length / 2.0) / sfreq + epochs.tmin

plt.figure()
plt.plot(w_times, np.mean(scores_windows, 0), label="Score")
plt.axvline(0, linestyle="--", color="k", label="Onset")
plt.axhline(0.5, linestyle="-", color="k", label="Chance")
plt.xlabel("time (s)")
plt.ylabel("classification accuracy")
plt.title("Classification score over time")
plt.legend(loc="lower right")
plt.show()

References

[1]

Zoltan J. Koles. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalography and Clinical Neurophysiology, 79(6):440–447, 1991. doi:10.1016/0013-4694(91)90163-X.

[2]

Gerwin Schalk, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R. Wolpaw. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6):1034–1043, 2004. doi:10.1109/TBME.2004.827072.

[3]

Ary L. Goldberger, Luis A. N. Amaral, Leon Glass, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, Roger G. Mark, Joseph E. Mietus, George B. Moody, Chung-Kang Peng, and H. Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 2000. doi:10.1161/01.CIR.101.23.e215.

Estimated memory usage: 0 MB