A static paradigm based on illusion-induced VEP for brain-computer interfaces

Abstract Objective. Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential is proposed for BCIs to enhance visual experience and practicality. Approach. This study explored the responses to baseline and illusion tasks including the rotating-tilted-lines illusion and rotating-snakes illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials and amplitude modulation of evoked oscillatory responses. Main results. The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s. Significance. The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.