Neural Feature Extraction
Neural Feature Extraction is a computational process within neural decoding that identifies and isolates biologically meaningful patterns or representations from raw neural signals by applying signal processing or machine learning techniques to transform high-dimensional neurophysiological data into lower-dimensional, interpretable features.
458
Publications
24.6K
Citations
1.8K
Authors
750
Institutions
Publications per year
2017–2026
264
Parents
458
NeuroKit2: A Python toolbox for neurophysiological signal processing
Dominique Makowski, Tam Pham, Zen Juen Lau et al. · Behavior Research Methods · 2021 · 1.3K citations · Full text
Hand classification of fMRI ICA noise components
Ludovica Griffanti, Gwenaëlle Douaud, Janine Bijsterbosch et al. · NeuroImage · 2016 · 630 citations · Full text
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Sheng Ao, Qingyong Hu, Bo Yang et al. · 2021 · 367 citations
1–5 of 458
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750
University of Oxford
Oxford, United Kingdom
8 h-index
28 publications
8.1K citations
University College London
London, United Kingdom
7 h-index
15 publications
516 citations
University of Pennsylvania
Philadelphia, United States
6 h-index
23 publications
1.1K citations

University of Science and Technology Beijing
Beijing, China
6 h-index
10 publications
279 citations

South China University of Technology
Guangzhou, China
6 h-index
19 publications
732 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
20 publications
746 citations
Tsinghua University
Beijing, China
6 h-index
20 publications
1K citations
University of California, Los Angeles
Los Angeles, United States
5 h-index
14 publications
1.2K citations
1–8 of 750
14 publications · 548 citations
12
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11 publications · 1.2K citations
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11 publications · 1.2K citations
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11 publications · 475 citations
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9 publications · 520 citations
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