A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high...

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Detalles Bibliográficos
Autores principales: Han, Jiuqi, Zhao, Yuwei, Sun, Hongji, Chen, Jiayun, Ke, Ang, Xu, Gesen, Zhang, Hualiang, Zhou, Jin, Wang, Changyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911500/
https://www.ncbi.nlm.nih.gov/pubmed/29713262
http://dx.doi.org/10.3389/fnins.2018.00217

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5911500/
https://www.ncbi.nlm.nih.gov/pubmed/29713262
http://dx.doi.org/10.3389/fnins.2018.00217

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