Discriminant Multitaper Component Analysis of EEG

This work extends Bilinear Discriminant Component Analysis to the case of oscillatory activity with allowed phase-variability across trials. The proposed method learns a spatial profile together with a multitaper basis which can integrate oscillatory power in a band-limited fashion. We demonstrate the method for predicting the handedness of a subject’s button press given multivariate EEG data. We show that our method learns multitapers sensitive to oscillatory activity in the 8-12Hz range with spatial filters selective for lateralized motor cortex. This finding is consistent with the well-known mu-rhythm, whose power is known to modulate as a function of which hand a subject plans to move, and thus is expected to be discriminative (predictive) of the subject’s response.