[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential]
- PMID: 36854561
- PMCID: PMC9989750
- DOI: 10.7507/1001-5515.202205090
[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential]
Abstract
Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.
稳态视觉诱发电位(SSVEP)近年来被广泛应用于脑-机接口(BCI)系统的研究中,SSVEP-BCI系统具有分类精度高、信息传输速率快和抗干扰能力强等优点。传统研究大多诱发低、中频段SSVEP响应作为系统控制信号,然而该频带的SSVEP可能导致受试者视觉疲劳甚至诱发癫痫。相比之下,尽管高频SSVEP-BCI幅值较低、响应微弱,但它提供了更舒适自然的交互方式,近年来也被研究人员广泛关注。本文针对近十年高频SSVEP-BCI相关研究,分别从范式和算法两方面进行归纳分析,最后对其应用前景和发展方向进行了讨论和展望。.
Keywords: Brain-computer interface; High frequency; Steady-state visual evoked potential.
Conflict of interest statement
利益冲突声明:本文全体作者均声明不存在利益冲突。
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References
-
- Chabuda A, Dovgialo M, Duszyk A, et al Successful BCI communication via high-frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers. Acta Neurobiol Exp. 2019;79(4):421–431. - PubMed
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