An Analysis on the Effect of Phase on the Performance of SSVEP-based BCIs

Abstract

In recent years, brain-computer interface (BCI) systems have emerged as a technology that can support a direct communication pathway between the human brain and external devices. Among the various neurophysiological phenomena that can be used to drive BCI systems, steady-state visual evoked potentials (SSVEPs) have gained increasing popularity because of the high information transfer rate (ITR) and high accuracy that these can provide. In this paper, an investigation on how the inclusion of phase information in the form of a novel proposed feature, the phase-weighted SNR (PWS) feature, can improve the performance of such setups is presented. Improvements in classification accuracies of up to 17% were obtained with the inclusion of phase information when compared to systems that rely solely on amplitude information.