Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, a...

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Detalles Bibliográficos
Autores principales: Chai, Rifai, Ling, Sai Ho, San, Phyo Phyo, Naik, Ganesh R., Nguyen, Tuan N., Tran, Yvonne, Craig, Ashley, Nguyen, Hung T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339284/
https://www.ncbi.nlm.nih.gov/pubmed/28326009
http://dx.doi.org/10.3389/fnins.2017.00103

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339284/
https://www.ncbi.nlm.nih.gov/pubmed/28326009
http://dx.doi.org/10.3389/fnins.2017.00103

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