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.

The optimization of coding stimulus is a cruial factor in the study of steady-state visual evoked potential (SSVEP)-based brain-computer interface(BCI).This study proposed an encoding approach named Multi-Symbol Time Division Coding (MSTDC). This approach is based on a protocol of maximizing the distance between neural responses, which aims to encode stimulation systems implementing any number of targets with finite stimulations of different frequencies and phases. Firstly, this study designed an SSVEP-based BCI system containing forty targets with this approach. The stimulation encoding of this system was achieved with four temporal-divided stimuli that adopt the same frequency of 30Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of 96.77±2.47% and an average information transfer rate (ITR) of 119.05±6.11 bits/min. This study also devised an SSVEP-based BCI system containing 72 targets and proposed a Template Splicing task-related component analysis (TRCA) algorithm that utilized the dataset of the previous system containing forty targets as the training dataset. The subjects acquired an average accuracy of 86.23±7.75% and an average ITR of 95.68±14.19 bits/min. It can be inferred that MSTDC can encode multiple targets with limited frequencies and phases of stimuli. Meanwhile, this protocol can be effortlessly expanded into other systems and sufficiently reduce the cost of collecting training data. This study provides a feasible technique for obtaining a comfortable SSVEP-based BCI with multiple targets while maintaining high information transfer rate.