Phase-based detection of intentional state for asynchronous brain-computer interface

An asynchronous brain-computer interface (BCI) is one of the crucial challenges in biomedical signal processing. In asynchronous BCIs, a state when a user does not intend to input commands needs to be distinguished from a state when he/she does. These states are called non-control (NC) state and intentional control (IC) state respectively. In this paper, a new phase-based method to discriminate between IC/NC states for steady-state visual evoked potential (SSVEP) based asynchronous BCIs is proposed. The method has a two-step tree structure: in the first step, a SSVEP frequency is recognized with canonical correlation analysis (CCA), and in the next step, the state of a user is detected as IC or NC with a classifier such as SVM using phase information. The proposed method was tested on six healthy subjects and has been proved to be reliable in terms of sensitivity and specificity.