Efficient dual-frequency SSVEP brain-computer interface system exploiting interocular visual resource disparities

Abstract

Here, we present a novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system with a unique human–computer interaction (HCI) feature that utilizes distinct visual resource disparities between the two eyes. By leveraging subtle differences in electroencephalography (EEG) responses due to binocular disparity, our system effectively distinguishes identical frequency targets. We conducted a two-phase validation: initial offline experiments confirmed EEG differences from interocular disparity, followed by global optimization to refine the SSVEP coding and spatial arrangement, allowing for 40 distinct targets within 20 frequency combinationes. A tailored binocular-task-related component analysis (bTRCA) algorithm was developed, achieving a maximum individual information transmission rate (ITR) of 338.32 bits/min offline and an average of 224.96 bits/min, with the online version reaching an average ITR of 234.13 bits/min. These results indicate that this system is a leading candidate for dual-frequency SSVEP BCIs, opening new prospects for HCI applications.