Independent Component Analysis

Independent Component Analysis is a statistical method for dimensionality reduction and feature transformation in unsupervised learning that separates a multivariate signal into additive, mutually independent components by maximizing their non-Gaussianity under linear independence constraints.

5.3K

Publications

426.5K

Citations

15.2K

Authors

3.6K

Institutions

Publications per year

2017–2026

1.4K

Authors

15.2K

  1. VD
    Vince D. Calhoun

    Mind Research Network, United States

    189 publications · 25.8K citations

    65

    h-index

  2. SM
    Scott Makeig

    University of California San Diego, United States

    67 publications · 45.3K citations

    44

    h-index

  3. TJ
    Terrence J. Sejnowski

    Salk Institute for Biological Studies, United States

    52 publications · 32.8K citations

    40

    h-index

  4. GD
    Godfrey D. Pearlson

    Yale University, United States

    56 publications · 12.3K citations

    39

    h-index

  5. AH
    Aapo Hyvärinen

    University of Helsinki, Finland

    63 publications · 27.6K citations

    39

    h-index

1–5 of 15.2K

Venues

  1. Human Brain Mapping

    168 publications · 24K citations

    71

    h-index

  2. NeuroImage

    137 publications · 15.6K citations

    58

    h-index

  3. PLoS ONE

    110 publications · 6.9K citations

    48

    h-index

  4. IEEE Transactions on Biomedical Engineering

    79 publications · 10.4K citations

    45

    h-index

  5. Frontiers in Human Neuroscience

    77 publications · 6.1K citations

    41

    h-index

1–5