A study on dynamic model of steady-state visual evoked potentials

Objective. Significant progress has been made in the past two decades to considerably improve the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, there are still some unsolved problems that may help us to improve BCI performance, one of which is that our understanding of the dynamic process of SSVEP is still superficial, especially for the transient-state response. Approach. This study introduced an antiphase stimulation method (antiphase: phase ), which can simultaneously separate and extract SSVEP and event-related potential (ERP) signals from EEG, and eliminate the interference of ERP to SSVEP. Based on the SSVEP signals obtained by the antiphase stimulation method, the envelope of SSVEP was extracted by the Hilbert transform, and the dynamic model of SSVEP was quantitatively studied by mathematical modeling. The step response of a second-order linear system was used to fit the envelope of SSVEP, and its characteristics were represented by four parameters with physical and physiological meanings: one was amplitude related, one was latency related and two were frequency related. This study attempted to use pre-stimulation paradigms to modulate the dynamic model parameters, and quantitatively analyze the results by applying the dynamic model to further explore the pre-stimulation methods that had the potential to improve BCI performance. Main results. The results showed that the dynamic model had good fitting effect with SSVEP under three pre-stimulation paradigms. The test results revealed that the parameters of SSVEP dynamic models could be modulated by the pre-stimulation baseline luminance, and the gray baseline luminance pre-stimulation obtained the highest performance. Significance. This study proposed a dynamic model which was helpful to understand and utilize the transient characteristics of SSVEP. This study also found that pre-stimulation could be used to adjust the parameters of SSVEP model, and had the potential to improve the performance of SSVEP-BCI.