Frequency recognition for SSVEP-based BCI with data adaptive reference signals

Abstract

Steady-state visual evoked potential (SSVEP) is an effective electrophysiological source to implement a brain-computer interface (BCI). In this paper, a novel frequency recognition method is introduced using two levels of reference signals derived from the training set of real world SSVEP signals with canonical correlation analysis (CCA). The first level reference signals are obtained by averaging the training trials of respective stimulus frequency. Standard CCA with thus obtained reference signals is applied to the training trails to measure the dominance of the stimulus frequency component. Several training trials containing more prominent target (stimulus) frequency component are selected as the second level reference signals. Both the obtained reference signals are used with CCA to derive an effective spatial filter for frequency recognition. The experimental results show that the proposed approach significantly improves the recognition accuracy of SSVEP as well as the information transfer rate (ITR) compared to the state-of-the-art recognition methods.