Feature fusion improves brain-interface paradigm based on steady state visual evoked potential blocking response

Abstract

The steady-state visual evoked potential blocking response (SSVEP-BR) is produced on an electroencephalogram (EEG) when the SSVEP is interrupted or abolished and allows augmentation of brain-computer interface (BCI) paradigms. Integration of the SSVEP-BR with the SSVEP enables an increase in the number of commands without the addition of further stimuli but refinement would improve performance. The current study evaluated SSVEP-BR and a novel method to combine multiple features and enhance the performance of frequency recognition and SSVEP-BR identification is proposed. Correlation features were extracted by filter bank canonical correlation analysis (FBCCA) and synchronization features by multivariate synchronization index (MSI) before being integrated. The performance of correlation features extracted by task-related component analysis (TRCA) were also evaluated. The novel integrated method was compared with FBCCA, MSI and TRCA for performance in frequency recognition and SSVEP-BR identification. The novel integrated method achieved higher classification accuracy than FBCCA and MSI for benchmark datasets when the sliding window was used for EEG data. However, the accuracy of TRCA was not stable when the sliding window was used. The novel integrated method produced an improvement in SSVEP-BR identification over FBCCA and MSI in the blocking dataset. TRCA was not found to be effective for SSVEP-BR identification. A novel integrated method is proposed which gives higher classification accuracy and more stable performance than FBCCA, MSI and TRCA for frequency recognition and SSVEP-BR identification when the EEG data sliding window was used and shows superior performance for the BCI paradigm based on SSVEP and SSVEP-BR.