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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Shranjeno v:
Bibliografske podrobnosti
izdano v: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
Jezik:Inglês
Izdano: Springer US 2018
Teme:
Original Article
Online dostop: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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