Spatio-Spectral Filter for Improved Classification of Single-Trial EEG
Steven Lemm, Benjamin Blankertz, Gabriel Curio and Klaus-Robert Müller
IEEE Trans. Biomed. Eng.
Data recorded in EEG based Brain-Computer Interfacing experiments is
generally very noisy, nonstationary and contaminated with artifacts,
that can deteriorate discrimination/classification methods. In this
work we extend the Common Spatial Pattern (CSP) algorithm with the
aim to alleviate these adverse effects. In particular we suggest an
extension of CSP to the state space, which utilizes the method of
delay embedding. As we will show, this allows for individually tuned
frequency filters at each electrode position and thus yields an
improved and more robust machine learning procedure. The advantages
of the proposed method over the currently state-of-the-art method
(CSP) are verified in terms of an improved information transfer rate
(bits per trial) on a set of EEG-recordings from experiments of
imagined limb movements.