Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA

Abstract

Canonical correlation analysis (CCA) has been widely used for frequency recognition in steady-state visual evoked potential (SSVEP) based brain–computer interfaces (BCIs). However, linear CCA-based methods may be insufficient given the complexity of EEG signals. A nonlinear feature extraction method based on deep multiset CCA (DMCCA) is proposed for SSVEP recognition to fully utilize the real EEG and constructed sine–cosine signals. In DMCCA, neural networks are trained to learn the nonlinear representations of multiple sets of EEG signals at the same frequency by maximizing the overall correlation within the representations and reference signals. Therefore, reference signals are augmented with the extracted features for frequency recognition. Finally, the proposed method is evaluated using SSVEP signals collected from 10 subjects. DMCCA-based method outperforms others in terms of classification accuracy compared with CCA- and multiset CCA-based methods. The proposed DMCCA-based method has substantial potential for improving the recognition performance of SSVEP signals.