Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intrac...

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Detalles Bibliográficos
Autores principales: Saboo, Krishnakant V., Varatharajah, Yogatheesan, Berry, Brent M., Kremen, Vaclav, Sperling, Michael R., Davis, Kathryn A., Jobst, Barbara C., Gross, Robert E., Lega, Bradley, Sheth, Sameer A., Worrell, Gregory A., Iyer, Ravishankar K., Kucewicz, Michal T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874617/
https://www.ncbi.nlm.nih.gov/pubmed/31758077
http://dx.doi.org/10.1038/s41598-019-53925-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874617/
https://www.ncbi.nlm.nih.gov/pubmed/31758077
http://dx.doi.org/10.1038/s41598-019-53925-5

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