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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發表在: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
格式: Artigo
語言:Inglês
出版: Springer US 2018
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Original Article
在線閱讀: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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