Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a nov...

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Detalles Bibliográficos
Autores principales: Nejedly, Petr, Cimbalnik, Jan, Klimes, Petr, Plesinger, Filip, Halamek, Josef, Kremen, Vaclav, Viscor, Ivo, Brinkmann, Benjamin H., Pail, Martin, Brazdil, Milan, Worrell, Gregory, Jurak, Pavel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6459786/
https://www.ncbi.nlm.nih.gov/pubmed/30105544
http://dx.doi.org/10.1007/s12021-018-9397-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6459786/
https://www.ncbi.nlm.nih.gov/pubmed/30105544
http://dx.doi.org/10.1007/s12021-018-9397-6

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