Recognition of high-frequency steady-state visual evoked potential for brain-computer interface

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current adv...

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Vydáno v:Sheng wu yi xue gong cheng xue za zhi Ročník 40; číslo 4; s. 683
Hlavní autoři: Luo, Ruixin, Dou, Xinyi, Xiao, Xiaolin, Wu, Qiaoyi, Xu, Minpeng, Ming, Dong
Médium: Journal Article
Jazyk:čínština
Vydáno: 25.08.2023
ISSN:1001-5515
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Abstract Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditio
AbstractList Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditio
Author Dou, Xinyi
Xu, Minpeng
Ming, Dong
Xiao, Xiaolin
Luo, Ruixin
Wu, Qiaoyi
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  givenname: Minpeng
  surname: Xu
  fullname: Xu, Minpeng
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  givenname: Dong
  surname: Ming
  fullname: Ming, Dong
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Title Recognition of high-frequency steady-state visual evoked potential for brain-computer interface
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