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

Authors

1.8K

  1. MZ
    Milin Zhang

    Tsinghua University, China

    4 publications · 212 citations

    4

    h-index

  2. XL
    Xilin Liu

    University of Pennsylvania, United States

    4 publications · 212 citations

    4

    h-index

  3. TH
    Timothy H. Lucas

    University of Pennsylvania, United States

    4 publications · 212 citations

    4

    h-index

  4. MK
    Martin Karafiát

    Brno University of Technology, Czechia

    4 publications · 95 citations

    4

    h-index

  5. AG
    Andrew G. Richardson

    University of Pennsylvania, United States

    4 publications · 212 citations

    4

    h-index

1–5 of 1.8K

Venues

  1. IEEE Access

    14 publications · 548 citations

    12

    h-index

  2. NeuroImage

    11 publications · 1.2K citations

    11

    h-index

  3. 11

    h-index

  4. Journal of Neural Engineering

    11 publications · 475 citations

    11

    h-index

  5. Frontiers in Neuroscience

    9 publications · 520 citations

    9

    h-index

1–5