Brain-computer Interface Signal Modeling
Brain-Computer Interface Signal Modeling is a computational framework that characterizes the transformation of neural electrophysiological activity into interpretable control signals for external devices, distinguished by its focus on mathematical representation and algorithmic decoding of brain-originated bioelectric patterns within real-time, closed-loop systems.
181
Publications
10.2K
Citations
607
Authors
272
Institutions
Publications per year
2017–2026
70
181
Correlation-based channel selection and regularized feature optimization for MI-based BCI
Jing Jin, Yangyang Miao, Ian Daly et al. · Neural Networks · 2019 · 335 citations
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272
Tsinghua University
Beijing, China
9 h-index
28 publications
2.2K citations
Institute for Infocomm Research
Singapore, Singapore
6 h-index
13 publications
1.1K citations

University of Essex
Colchester, United Kingdom
6 h-index
9 publications
534 citations
University of Pittsburgh
Pittsburgh, United States
6 h-index
12 publications
1.2K citations
East China University of Science and Technology
Shanghai, China
5 h-index
18 publications
2.1K citations
University of California San Diego
San Diego, United States
5 h-index
14 publications
1.7K citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
10 publications
1K citations
Graz University of Technology
Graz, Austria
4 h-index
13 publications
1.3K citations
1–8 of 272
11 publications · 636 citations
11
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11 publications · 828 citations
10
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9 publications · 810 citations
8
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8 publications · 1K citations
8
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9 publications · 329 citations
7
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1–5