A dynamically optimized time-window length for SSVEP based hybrid BCI-VR system

Identification accuracy and information transfer rate are two critical performance indicators of the brain-computer interfaces and virtual reality (BCI_VR) hybrid system. A parameter closely related to these two in-dicators is the time-window length of the data. When the time-window value is too large, it is not conducive to the information transmission rate. Similarly, it affects recognition accuracy when the value is too small. How to set the time-window value reasonably to achieve a balance between recognition accuracy and information transfer rate to ensure the high-performance of the hybrid system is very important. In this paper, a dynamic time-window length optimized mechanism is proposed, which decides whether to change the time-window value of different subjects or trials according to the online performance of the hybrid system. Ten participants took part in the online experiment with an average accuracy of 92.07% and an average information transfer rate of 34.367 bits/min. Compared with the time-window adjustment method based on fixed-step in the existing study, the proposed method has achieved a 10.51% increase in information transfer rate and a 5.66% reduction in average detection time under the same experimental flow.