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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Bibliografiske detaljer
Udgivet i: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
Format: Artigo
Sprog:Inglês
Udgivet: Springer US 2018
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Original Article
Online adgang: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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