Comparing Features for Classification of MEG Responses to Motor Imagery
Background Motor imagery (MI) with real time neurofeedback could be a viable approach, e.g. in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well suited for recording oscillatory brain signals. MI is known to modulate 10 and 20 Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left and right hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left or right hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI related features were power spectral density (PSD) Morlet wavelets, short time Fourier transform (STFT) common spatial patterns (CSP) filter bank common spatial patterns (FBCSP) spatio spectral decomposition (SSD) and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5 fold cross validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter session left vs right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left vs right (mean 73.7% and MI vs rest (mean 81.3% classification. CSP+Morlet yielded the best mean accuracy in inter session left vs right classification (mean 69.1% There were large inter subject differences in classification accuracy, and the level of the 20 Hz suppression correlated significantly with the subjective MI vs rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor level decoding of MI from single trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.
PloS one