Author(s): Liang, LY (Liang, Liyan); Bin, GY (Bin, Guangyu); Chen, XG (Chen, Xiaogang); Wang, YJ (Wang, Yijun); Gao, SK (Gao, Shangkai); Gao, XR (Gao, Xiaorong)

Source: JOURNAL OF NEURAL ENGINEERING Volume: 18 Issue: 6 Article Number: 066040 DOI: 10.1088/1741-2552/ac40a1 Published: DEC 2021

Abstract: Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region. Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. Main results. In the 12-target online experiment, after a short model estimation training, all 16 subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6 +/- 20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2 +/- 14.7%, and the information transfer rate from 14.2 +/- 6.4 bits min(-1) to 17.8 +/- 5.7 bits min(-1). Significance. These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.

Accession Number: WOS:000735174900001

PubMed ID: 34875637

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Chen, Xiaogang                  0000-0002-5334-1728

Liang, Li yan                  0000-0003-0909-7200

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ac40a1