Dimensionality Reduction For Decoding

Dimensionality Reduction for Decoding is a computational method that transforms high-dimensional neural activity data into a lower-dimensional latent space while preserving task-relevant informational structure to improve the accuracy and efficiency of neural decoding.

31

Publications

1.7K

Citations

129

Authors

84

Institutions

Publications per year

2017–2026

18

Authors

129

  1. JL
    Jinglei Lv

    University of Georgia, United States

    2 publications · 190 citations

    2

    h-index

  2. YS
    Yan Shi

    Northeast Electric Power University, China

    2 publications · 190 citations

    2

    h-index

  3. XL
    Xiangmin Lun

    Northeast Electric Power University, China

    2 publications · 190 citations

    2

    h-index

  4. SJ
    Shuyue Jia

    Northeast Electric Power University, China

    2 publications · 190 citations

    2

    h-index

  5. YH
    Yimin Hou

    Northeast Electric Power University, China

    2 publications · 190 citations

    2

    h-index

1–5 of 129

Venues

  1. NeuroImage

    4 publications · 457 citations

    4

    h-index

  2. Journal of Neural Engineering

    3 publications · 79 citations

    3

    h-index

  3. IEEE Transactions on Information Theory

    1 publications · 64 citations

    1

    h-index

  4. Frontiers in Neuroinformatics

    1 publications · 15 citations

    1

    h-index

  5. Brain Informatics

    1 publications · 11 citations

    1

    h-index

1–5