A 120-target brain-computer interface based on code-modulated visual evoked potentials

Background:In recent years, numerous studies on the brain-computer interface (BCI) have been published. However, the number of targets in most of the existing studies was not enough for many practical applications. New method:To achieve highly efficient communications, this study proposed a 120-target BCI system based on code-modulated visual evoked potentials (c-VEPs). Four 31-bit pseudorandom codes were used, and each code generated 30 targets by cyclic shift with a lag of 1 bit. Results:In the online experiments, subjects could select one target in 1.04 s (0.52 s for stimulation and 0.52 s for gaze shifting) with an average information transfer rate (ITR) of 265.74 bits/min. Comparison with existing method: The proposed system achieved more targets and higher ITR than other recent cVEP based studies. which attributes to the optimal code combination and the 1-bit lag. Conclusion:The results illustrate that the proposed BCI system can achieve a high ITR with a short stimulation time. In addition, the c-VEP paradigm can shorten the training time, which ensures practicality in real applications.