A Novel Binocular-Encoded SSVEP Framework for Efficient VR-Based Brain-Computer Interface

This paper presents a novel binocular-encoded SSVEP (beSSVEP) method, leveraging binocular vision in virtual reality (VR) to enhance brain-computer interface (BCI) applications. We introduce the Binocular Periodically Repeated Component Analysis (bPRCA) algorithm, designed to address the unique characteristics of binocular-encoded targets, which include combinations of monocular single-frequency SSVEP units or void units, with frequency units being reused multiple times in the encoded interface. To further optimize performance, we propose the Fusion Component Analysis (FusionCA) framework, which integrates bPRCA with Task-Related Component Analysis (TRCA), effectively utilizing both steady-state periodic components and cross-trial aperiodic components. Experimental results demonstrate that ensemble-FusionCA achieves the highest information transfer rate (ITR) with an average accuracy of 71.39% and an ITR of 138.50 bits/min at 0.4 seconds, among the comparison with ensemble-bPRCA and ensemble-TRCA. Compared to traditional SSVEP approaches, beSSVEP significantly enhances frequency utilization, making VR-BCI systems more efficient and practical. This study highlights the application of physiological mechanisms of binocular vision to improve BCI systems, offering a new perspective for developing fast and scalable brain-computer interactions in VR environments.