Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms

Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power dis...

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Detalles Bibliográficos
Autores principales: Aquino-Brítez, Diego, Ortiz, Andrés, Ortega, Julio, León, Javier, Formoso, Marco, Gan, John Q., Escobar, Juan José
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002580/
https://www.ncbi.nlm.nih.gov/pubmed/33802684
http://dx.doi.org/10.3390/s21062096

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8002580/
https://www.ncbi.nlm.nih.gov/pubmed/33802684
http://dx.doi.org/10.3390/s21062096

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