Advancing SSVEP-based brain-computer interfaces: a novel approach using cross-subject multi-modal fusion technique

Abstract

Brain-computer interfaces (BCIs) represent an innovative paradigm for device control and communication, relying solely on the analysis of brain activity. Steady-state visually evoked potentials (SSVEPs), characterized by neurophysiological responses synchronized with periodic visual stimuli, have gained prominence in BCI research due to their high information transfer rates (ITRs) and minimal user training requirements. However, the translation of SSVEP-based BCIs into practical applications faces challenges stemming from variations in user responses and stimuli. To address these issues, this study introduces a groundbreaking methodology known as the cross-subject multi-modal fusion technique (CMFT). CMFT revolutionizes template design by creating invariant templates resilient to user and stimulus differences, thereby enhancing SSVEP detection across diverse subjects and stimuli. The implications of this research extend to various fields, including assistive technologies, human-computer interaction, and cognitive neuroscience. CMFT presents a promising solution to make SSVEP-based BCIs more practical and widely applicable. The methodology involves intricate steps, including spatial filters, data pre-processing, and template generation, ensuring precise SSVEP detection. Through CMFT, this study contributes to advancing the effectiveness and versatility of SSVEP-based BCIs, fostering improved accessibility and interaction in a range of domains.