LAUSR

Various improved canonical correlation analysis (CCA) methods were developed for enhancing the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Among them, the method combining CCA spatial filters from sine-cosine references and individual templates yielded the highest performance. However, the CCA aims to optimize the correlation between two sets of variables rather than the signal-to-noise ratio (SNR) of the SSVEP signals, upon which the performance of an SSVEP-based BCI depends mainly. In this paper, a novel algorithm, namely, maximum signal fraction analysis (MSFA), is proposed for creating spatial filters based on individual training data. The spatial filter for a specific stimulus target is estimated by directly maximizing the averaged SNR of the observed signals across multiple trials. An individual template is calculated for each target by averaging training signals of multiple trials. Target recognition is based on template matching between filtered template signals and a single-trial testing signal. Classification performance of the MSFA-based method was evaluated on a benchmark dataset and compared with that of the CCA-based methods. The results suggest that the proposed MSFA method significantly outperforms the CCA-based methods in terms of classification accuracy, and thus, it has great potential to be applied in the real-life SSVEP-based BCI systems.