Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wr...

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Detalles Bibliográficos
Autores principales: Wang, Jin-Jia, Xue, Fang, Li, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354735/
https://www.ncbi.nlm.nih.gov/pubmed/25802861
http://dx.doi.org/10.1155/2015/703768

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354735/
https://www.ncbi.nlm.nih.gov/pubmed/25802861
http://dx.doi.org/10.1155/2015/703768

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