An algorithm for brain computer interfacing based on phase synchronization spatial patterns

Abstract

Brain computer interfacing (BCI) is an interdis- ciplinary area that connects biomedical knowledge on brain

physiology with the use of effective computational techniques

for mental task discrimination. One of the most successful

algorithms in BCI is the method of common spatial patterns

(CSP). The spatial filters computed using this method are based

on a joint diagonalization of the covariance matrices of two

classes of data. However, in the standard CSP implementation

the influence of amplitude and phase characteristics are not

distinguished. In order to investigate phase synchronization

links for the scope of task discrimination, we propose a

variant of the method of CSP that depends explicitly on phase

synchronization information. The phase-based CSP (P-CSP)

was tested on simulated data to analyze if the implemented

method yields significant patterns of phase interaction. The

algorithm was also used on real EEG data for the classification

of two motor imagery tasks and results were compared to

those obtained using the phase locking value (PLV). The results

obtained indicate that using the method of P-CSP with a few

spatial filters can yield classification rates close to or exceeding

the best classification rate obtained from the PLV measures for

any pair of channels.