An Edge-Modalized Visual Stimulation Paradigm for SSVEP-Based Brain-Computer Interface

With the increasing number of people with disabilities and other social problems, brain-computer interface (BCI) plays an increasingly important role in the field of medical rehabilitation. In this paper, a novel edge-modalized visual stimulation (EMVS) method, which can generate more visual stimuli at a limited stimulus frequency, is proposed for a BCI system based on steady-state visual evoked potential (SSVEP). In the offline experiment, all participants can use this paradigm to induce recognizable EEG signals, with a high average classification accuracy (ACC) of 88.13± 11.95% and an average information transfer rate (ITR) of 18.73 ± 6.18 bits min^-1. Therefore, the paradigm proposed in this study can successfully stimulate the brain to produce recognizable response patterns, providing a new perspective for us to deeply understand the brain response mechanism, and providing a physiological basis for the realization of multiple classification of edge-modalized visual evoked potential (EMVEP).