VMD-FBCCA classification method for SSVEP brain–computer interfaces

Abstract

A steady-state visually evoked potential (SSVEP) is a brain response to specific frequencies of visual stimuli, including their harmonic frequencies. However, this signal is susceptible to interference from spontaneous $\alpha$ and $\beta$ rhythms in electroencephalography (EEG) signals because they overlap from 8 to 40 Hz. This can reduce the recognition accuracy of SSVEP brain–computer interfaces (BCIs). To address this problem, a variational mode decomposition–based filter bank canonical correlation analysis (VMD-FBCCA) algorithm is proposed, which integrates the adaptive characteristics of VMD and the training-free nature of the FBCCA algorithm. First, the EEG signal of each channel is transformed into intrinsic mode functions (IMFs) by the VMD algorithm, which extracts frequency components of the SSVEP from each IMF. Next, a particle swarm algorithm is employed to optimize the weights of the IMFs and reconstruct the EEG signals. This reconstruction selectively enhances the IMFs in the target SSVEP frequency band while suppressing interference from other bands. Finally, the reconstructed EEG is classified using FBCCA to decode the SSVEP-BCI signal. To evaluate its effectiveness, the proposed algorithm is tested on datasets from the BCI Competition. The results demonstrate that VMD-FBCCA outperforms FBCCA, showing improvements in both the average recognition accuracy $\left(6.04\%\right)$ and information transmission rate (8.91 bits/min). Moreover, the best recognition accuracy achieved for individual subjects is enhanced by $29.17\%$.