Brain-computer Interface Training Protocols

Brain-Computer Interface Training Protocols is a systematic framework comprising standardized procedures and adaptive algorithms designed to guide the calibration, user learning, and performance optimization processes in brain-computer interface systems, distinguishing it from general neurofeedback or brain stimulation protocols by its specific integration of real-time neural signal decoding and closed-loop feedback tailored to assist users in effectively modulating their neural activity for direct device control.

70

Publications

3.8K

Citations

306

Authors

149

Institutions

Publications per year

2017–2026

32

Authors

306

  1. GM
    Gernot Müller-Putz

    Graz University of Technology, Austria

    5 publications · 391 citations

    5

    h-index

  2. FL
    Fabien Lotte

    Laboratoire Bordelais de Recherche en Informatique, France

    5 publications · 588 citations

    5

    h-index

  3. RS
    Reinhold Scherer

    Graz University of Technology, Austria

    4 publications · 197 citations

    4

    h-index

  4. AK
    Andrea Kübler

    University of Würzburg, Germany

    4 publications · 297 citations

    4

    h-index

  5. CJ
    Camille Jeunet

    Laboratoire Bordelais de Recherche en Informatique, France

    3 publications · 250 citations

    3

    h-index

1–5 of 306

Venues

  1. Frontiers in Human Neuroscience

    5 publications · 477 citations

    5

    h-index

  2. Journal of Neural Engineering

    4 publications · 354 citations

    4

    h-index

  3. 3

    h-index

  4. PLoS ONE

    3 publications · 229 citations

    3

    h-index

  5. Multimedia Tools and Applications

    2 publications · 88 citations

    2

    h-index

1–5