Review of brain–computer interface based on steady‐state visual evoked potential. Issue 4 (December 2022)

• Record Type: Journal Article
• Authors: Liu, Siyu; Zhang, Deyu; Liu, Ziyu; Liu, Mengzhen; Ming, Zhiyuan; Liu, Tiantian; Suo, Dingjie; Funahashi, Shintaro; Yan, Tianyi
• Is Part Of: Brain science advances. Volume 8:Issue 4(2022)
• Page Start: 258
• Page End: 275
• Publication Date: 2022-12
• Subjects: steady‐state visual evoked potential -- brain–computer interface -- canonical correlation analysis -- decoding algorithm; Neurosciences -- Periodicals; Medical innovations -- Periodicals; Medical innovations; Neurosciences; Periodicals; 616.8005
• DOI: 10.26599/BSA.2022.9050022
• Languages: English
• ISSNs: 2096-5958

Abstract

The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.