Independent Component Analysis Features

Independent Component Analysis Features is a feature extraction technique that isolates statistically independent underlying sources from multivariate data by maximizing non-Gaussianity and mutual independence among the derived components.

493

Publications

57.4K

Citations

1.7K

Authors

692

Institutions

Publications per year

2017–2026

118

Authors

1.7K

  1. VD
    Vince D. Calhoun

    Mind Research Network, United States

    14 publications · 1.1K citations

    13

    h-index

  2. AH
    Aapo Hyvärinen

    University of Helsinki, Finland

    10 publications · 2.1K citations

    10

    h-index

  3. GR
    Ganesh R. Naik

    University of Technology Sydney, Australia

    6 publications · 175 citations

    6

    h-index

  4. DK
    Dinesh Kumar

    RMIT University, Australia

    5 publications · 133 citations

    5

    h-index

  5. JS

    5

    h-index

1–5 of 1.7K

Venues

  1. Neurocomputing

    9 publications · 366 citations

    9

    h-index

  2. NeuroImage

    9 publications · 970 citations

    9

    h-index

  3. Electronics Letters

    6 publications · 145 citations

    6

    h-index

  4. IEEE Transactions on Biomedical Engineering

    6 publications · 779 citations

    6

    h-index

  5. IEEE Access

    5 publications · 224 citations

    5

    h-index

1–5