Author(s): Ye, XC (Ye, Xiaochen); Yang, C (Yang, Chen); Chen, YH (Chen, Yonghao); Wang, YJ (Wang, Yijun); Gao, XR (Gao, Xiaorong); Zhang, HX (Zhang, Hongxin)

Source: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Volume: 30 Pages: 1693-1704 DOI: 10.1109/TNSRE.2022.3183087 Published: 2022

Abstract: 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 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.

Accession Number: WOS:000818860400005

PubMed ID: 35714087

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Chen, Yonghao                  0000-0002-8788-830X

ISSN: 1534-4320

eISSN: 1558-0210

Full Text: https://ieeexplore.ieee.org/document/9799765