A L1 normalization enhanced dynamic window method for SSVEP-based BCIs

Abstract

Background: Filter bank canonical correlation analysis (FBCCA) has been widely applied to detect the frequency components of steady-state visual evoked potential (SSVEP). FBCCA with dynamic window (FBCCA-DW) is recently proposed to improve its performance. FBCCA-DW adaptively chooses a proper window length based on the signal-to-noise ratio (SNR) of SSVEP signals. It takes the output of FBCCA to evaluate the SNR of SSVEP signals, by using the softmax function and cost function. In practice, SSVEP signals always contain task-unrelated electroencephalogram (EEG), which degrades the SSVEP task. When the power of task-unrelated EEG changes, there would be an offset in the output of FBCCA. However, due to the insensitivity of softmax function to the offset, the SNR in FBCCA-DW ignores the interference of the task-unrelated EEG. Therefore, FBCCA-DW will analyze SSVEP signals at an inappropriate window length.

New method: To solve the issue, we replace the softmax function with the L1 normalization, which could respond a reasonable SNR to the offset. Since the proposed method takes task-unrelated EEG into account, it could choose a more appropriate window length.

Results: We comprehensively validate the proposed method on three publicly available SSVEP datasets. The results indicate that the proposed method could improve the performance significantly.

Comparison with existing methods: The proposed method outperforms FBCCA and FBCCA-DW in terms of information transfer rate (ITR).

Conclusions: The proposed method enhances the correlation between the window length and the credibility of the recognition result. It shows its potential for practical applications in complex environments.

Keywords: Brain-computer interface (BCI); Canonical correlation analysis (CCA); Dynamic window; Electroencephalogram (EEG); Filter bank; Steady-state visual evoked potential (SSVEP).