Evidence map›Paper›PMID 37666758›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023

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

Ruixin Luo, Xinyi Dou, Xiaolin Xiao, Qiaoyi Wu, Minpeng Xu, Dong Ming

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ruixin LuoSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
Xinyi DouAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P. R. China.
Xiaolin XiaoSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
Qiaoyi WuSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
Minpeng XuSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.
Dong MingSchool of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.

Indexed as

Brain-Computer InterfacesAlgorithmsDiscriminant AnalysisElectroencephalographyEvoked Potentials, VisualHumansBrain-computer interfaceDecoding algorithmHigh-frequencySteady-state visual evoked potential

Identifiers

PMID37666758
PMCPMC10477378

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.