Brain–Computer Interface Signal Analysis is a specialized methodological framework within neural data analysis that distinguishes itself by focusing on the extraction, interpretation, and transformation of neural signals acquired from brain–computer interfaces into actionable commands or cognitive state inferences.
1.2K
Publications
83.7K
Citations
3.2K
Authors
1.1K
Institutions
Publications per year
2017–2026
439
Parents
Publications
1.2K
1–5 of 1.2K
Authors
3.2K
- BB
27
h-index
- XG
23
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- NB
22
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- SG
21
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- GM
21
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1–5 of 3.2K
Institutions
1.1K
Graz University of Technology
Graz, Austria
39 h-index
168 publications
30K citations
University of Tübingen
Tübingen, Germany
31 h-index
148 publications
31.4K citations
Technische Universität Berlin
Berlin, Germany
29 h-index
114 publications
18.6K citations
University of California San Diego
San Diego, United States
28 h-index
89 publications
8.8K citations
Wadsworth Center
Albany, United States
27 h-index
76 publications
25.7K citations
Tsinghua University
Beijing, China
27 h-index
134 publications
18.8K citations
New York State Department of Health
Albany, United States
24 h-index
64 publications
24.5K citations
University of Essex
Colchester, United Kingdom
22 h-index
77 publications
2.4K citations
1–8 of 1.1K
Venues
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
77 publications · 10.5K citations
51
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- Journal of Neural Engineering
98 publications · 8.7K citations
46
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- IEEE Transactions on Biomedical Engineering
36 publications · 8.4K citations
29
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- PLoS ONE
28 publications · 2.4K citations
22
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- Journal of Neuroscience Methods
29 publications · 2.5K citations
22
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