A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Jiabei Tang; Minpeng Xu; Zheng Liu; Jingjuan Qiao; Shuang Liu; Shanguang Chen; Tzyy-Ping Jung; Dong Ming

doi:10.1109/ACCESS.2020.3012283

IEEE Access (Jan 2020)

A Brain-Computer Interface Based on Multifocal SSVEPs Detected by Inter-Task-Related Component Analysis

Jiabei Tang,
Minpeng Xu,
Zheng Liu,
Jingjuan Qiao,
Shuang Liu,
Shanguang Chen,
Tzyy-Ping Jung,
Dong Ming

Affiliations

Jiabei Tang: ORCiD; Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Minpeng Xu: Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Zheng Liu: ORCiD; Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Jingjuan Qiao: Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Shuang Liu: Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
Shanguang Chen: Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Tzyy-Ping Jung: ORCiD; Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
Dong Ming: ORCiD; Department of Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China

DOI: https://doi.org/10.1109/ACCESS.2020.3012283
Journal volume & issue: Vol. 8
pp. 138539 – 138550

Abstract

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Multifocal steady-state visual evoked potentials (mfSSVEPs) have been successfully applied to assess visual field loss in glaucoma. However, the potential of mfSSVEPs for command control has not been fully explored yet. It is significant to detect single-trial mfSSVEPs and establish a brain-computer interface (BCI) system. This study designed a stimulating paradigm that contains 32 targets, with each target composed of five fan-shaped flickers in a circle. The five flickers were modulated by five frequencies and formed a five-bit binary encoding system through controlling the ON/OFF state of each flicker. Twelve subjects participated in an offline and an online experiments. Inter-task-related component analysis (iTRCA) combined with a probabilistic model was proposed for target recognition. Notably, the training data needed for calibration corresponded to only six out of the 32 targets. It was found that the increasing number of flickers showed a negative impact on the mfSSVEP signal. The accuracy reached 80.9% ± 11.7% on average with a peak of 95.3% by iTRCA, which was significantly higher than that by a traditional method. The results indicate that the proposed stimulation and algorithm are effective for encoding and decoding BCI commands. Therefore, the mfSSVEP-based BCI enables the augmentation of the BCI instruction set without any burden of collecting extra training data.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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