Eeg Signal Processing

EEG Signal Processing is a computational methodology that transforms raw electroencephalographic signals into analyzable representations by applying signal filtering, artifact removal, feature extraction, and noise reduction techniques to enhance neural activity patterns for clinical or research interpretation.

8.6K

Publications

572.4K

Citations

20.9K

Authors

4.5K

Institutions

Publications per year

2017–2026

3.7K

Authors

20.9K

  1. UR
    U. Rajendra Acharya

    Ngee Ann Polytechnic, Singapore

    55 publications · 6.9K citations

    43

    h-index

  2. TJ
    Tzyy‐Ping Jung

    University of California San Diego, United States

    71 publications · 14K citations

    42

    h-index

  3. RB
    Ram Bilas Pachori

    Indian Institute of Technology Indore, India

    57 publications · 5.7K citations

    39

    h-index

  4. AC
    Andrzej Cichocki

    RIKEN Center for Brain Science, Japan

    61 publications · 4.8K citations

    35

    h-index

  5. SM
    Scott Makeig

    University of California San Diego, United States

    51 publications · 41.1K citations

    35

    h-index

1–5 of 20.9K

Venues

  1. Electroencephalography and Clinical Neurophysiology

    239 publications · 23.9K citations

    82

    h-index

  2. IEEE Transactions on Biomedical Engineering

    272 publications · 28.5K citations

    81

    h-index

  3. NeuroImage

    179 publications · 25.6K citations

    75

    h-index

  4. Clinical Neurophysiology

    169 publications · 19.9K citations

    72

    h-index

  5. 63

    h-index

1–5