Low-frequency SSVEP stimuli with 20%-pixel density can induce larger EEG and fNIRS responses

The brain-computer interface based on steady-state visual evoked potential (SSVEP) has received increasing attention due to its high information transfer rate and low subject variation. A major challenge of current SSVEP-BCI is the uncomfortableness and fatigue induced by the strong visual flicker. Thus, it is of vital importance to optimize SSVEP stimuli for a better user experience. Reducing the pixel density of stimuli is a promising method to improve SSVEP. However, it remains unknown how the neural responses would be when faced with low-pixel density stimuli, and it is also unclear whether the corresponding decoding accuracy can be improved or not. Hereto, this study investigated neural responses induced by the stimuli with distinct pixel densities (1%, 10%, 20%, 60%, 100%) under both low (8Hz, 15Hz) and high frequencies (33Hz, 40Hz, 60Hz), responses from parietal-occipital area were recorded by functional near-infrared spectroscopy (fNIRS) and electroencephalo- gram (EEG) concurrently, aiming to have a better understanding of low-pixel-density-related responses. As a result, the behavioral performance showed that the comfort index inclined as the pixel density became lower. EEG and fNIRS signal analysis indicated that 20%-pixel induced larger EEG and fNIRS response than most densities in the low-frequency band. As to classification, comparing to the 100%, classification accuracy of 20%-pixel density classifies significantly better in low-frequency and high-frequency bands, whether in EEG, fNIRS, or hybrid. The maximum classification accuracy of 20%-density can reach 97.66% in hybrid binary classification, with 3.77% more than 100% density. This research provides a theoretical and technical basis for developing user-friendly SSVEP-BCI.