Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance

The development of validated algorithms for automated handling of artifacts is essential for reliable and fast processing of EEG signals. Recently, there have been methodological advances in designing machine-learning algorithms to improve artifact detection of trained professionals who usually meti...

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Detalles Bibliográficos
Autores principales: Diachenko, Marina, Houtman, Simon J., Juarez-Martinez, Erika L., Ramautar, Jennifer R., Weiler, Robin, Mansvelder, Huibert D., Bruining, Hilgo, Bloem, Peter, Linkenkaer-Hansen, Klaus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2022
Materias:
Research Article: Methods/New Tools
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532020/
https://www.ncbi.nlm.nih.gov/pubmed/36104277
http://dx.doi.org/10.1523/ENEURO.0160-22.2022

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532020/
https://www.ncbi.nlm.nih.gov/pubmed/36104277
http://dx.doi.org/10.1523/ENEURO.0160-22.2022

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