Implementing a calibration-free SSVEP-based BCI system with 160 targets

Abstract

Objective. Steady-state visual evoked potential (SSVEP) is an essential paradigm of electroencephalogram based brain–computer interface (BCI). Previous studies in the BCI research field mostly focused on enhancing classification accuracy and reducing stimuli duration. This study, however, concentrated on increasing the number of available targets in the BCI systems without calibration. Approach. Motivated by the idea of multiple frequency sequential coding, we developed a calibration-free SSVEP–BCI system implementing 160 targets by four continuous sinusoidal stimuli that lasted four seconds in total. Taking advantage of the benchmark dataset of SSVEP–BCI, this study optimized an arrangement of stimuli sequences, maximizing the response distance between different stimuli. We proposed an effective classification algorithm based on filter bank canonical correlation analysis. To evaluate the performance of this system, we conducted offline and online experiments using cue-guided selection tasks. Eight subjects participated in the offline experiments, and 12 subjects participated in the online experiments with real-time feedbacks. Main results. Offline experiments indicated the feasibility of the stimulation selection and detection algorithms. Furthermore, the online system achieved an average accuracy of 87.16 ± 11.46% and an information transfer rate of 78.84 ± 15.59 bits min−1. Specifically, seven of 12 subjects accomplished online experiments with accuracy higher than 90%. This study proposed an intact solution of applying numerous targets to SSVEP-based BCIs. Results of experiments confirmed the utility and efficiency of the system. Significance. This study firstly provides a calibration-free SSVEP–BCI speller system that enables more than 100 commands. This system could significantly expand the application scenario of SSVEP-based BCI. Meanwhile, the design criterion can hopefully enhance the overall performance of the BCI system. The demo video can be found in the supplementary material available online at stacks.iop.org/JNE/18/046094/mmedia.