EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder...

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Detalles Bibliográficos
Autores principales: Dai, Mengxi, Zheng, Dezhi, Na, Rui, Wang, Shuai, Zhang, Shuailei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387242/
https://www.ncbi.nlm.nih.gov/pubmed/30699946
http://dx.doi.org/10.3390/s19030551

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387242/
https://www.ncbi.nlm.nih.gov/pubmed/30699946
http://dx.doi.org/10.3390/s19030551

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