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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Detalhes bibliográficos
Publicado no:Neuroinformatics
Main Authors: 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: Artigo
Idioma:Inglês
Publicado em: Springer US 2018
Assuntos:
Original Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6459786/
https://ncbi.nlm.nih.gov/pubmed/30105544
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s12021-018-9397-6
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