Multisymbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 1693 - 1704
• Main Authors: Ye, Xiaochen, Yang, Chen, Chen, Yonghao, Wang, Yijun, Gao, Xiaorong, Zhang, Hongxin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Brain; Brain-computer interface; Codes; Coding; Computer applications; Datasets; Encoding; Frequency modulation; high-frequency; Human-computer interface; Implants; Information transfer; multi-target; Neural coding; Optimization; phase modulation; Phases; Steady state; steady-state visual evoked potential; Stimulation; Symbols; Time division; time division coding; Training; Visual evoked potentials; Visual stimuli; Visualization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3183087

The optimization of coding stimulus is a crucial 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 30 Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of <inline-formula> <tex-math notation="LaTeX">96.77 \pm 2.47 </tex-math></inline-formula>% 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.