Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs

Journal of Neural Engineering
Jing JiangDong Ming

Abstract

Electroencephalography (EEG) is a non-linear and non-stationary process, as a result, its features are unstable and often vary in quality across trials, which poses significant challenges to brain-computer interfaces (BCIs). One remedy to this problem is to adaptively collect sufficient EEG evidence using dynamic stopping (DS) strategies. The high-speed steady-state visual evoked potential (SSVEP)-based BCI has experienced tremendous progress in recent years. This study aims to further improve the high-speed SSVEP-based BCI by incorporating the DS strategy. This study involves the development of two different DS strategies for a high-speed SSVEP-based BCI, which were based on the Bayes estimation and the discriminant analysis, respectively. To evaluate their performance, they were compared with the conventional fixed stopping (FS) strategy using simulated online tests on both our collected data and a public dataset. Two most effective SSVEP recognition methods were used for comparison, including the extended canonical correlation analysis (CCA) and the ensemble task-related component analysis (TRCA). The DS strategies achieved significantly higher information transfer rates (ITRs) than the FS strategy for both datasets, improvi...Continue Reading

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Citations

May 22, 2019·Journal of Neural Engineering·Chi Man WongAgostinho Rosa
Jan 15, 2020·PloS One·Mohammad Hadi MehdizavarehHamid Soltanian-Zadeh
Jan 17, 2020·Journal of Neural Engineering·Thiago Bulhões da Silva CostaCristiano Panazio
Oct 16, 2019·Journal of Neural Engineering·Yufeng KeDong Ming
May 29, 2020·Biomedizinische Technik. Biomedical Engineering·Wenqiang Yan, Guanghua Xu
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Dec 14, 2018·Journal of Neural Engineering·Anti IngelRaul Vicente
May 8, 2020·Journal of Neural Engineering·Yonghao ChenXiaorong Gao

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