Lead selection for SSVEP-based brain-computer interface

SSVEP-based brain-computer interface (BCI) has potential advantage of high information transfer rate. However, individual difference greatly affects its practical applications. This paper presents a method of lead selection to improve the applicability of SSVEP-based BCI system. Independent component analysis (ICA) is employed to decompose EEGs over visual cortex into SSVEP signal and background noise. Optimal bipolar lead is selected by comparing signal correlation and noise correlation between different channels. The system with one optimal bipolar lead has reached an average transfer rate about 42bits/min for normal subjects. It has also been successfully applied to an environmental controller for the motion-disabled.