Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic-Reference-Cited by-同舟云学术

Multivariate autoregressive models and kernel learning algorithms for classifying driving mental fatigue based on electroencephalographic

Published:2011-03 Issue:3 Volume:38 Page:1859-1865
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zhao Chunlin,Zheng Chongxun,Zhao Min,Tu Yaling,Liu Jianping

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference23 articles.

1. Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks;Anderson;IEEE Transactions on Biomedical Engineering,1998

2. A complex analysis of the driver behavior from simulated driving focused on fatigue detection classification;Bouchner;WSEAS Transactions on Systems,2006

3. A tutorial on support vector machines for pattern recognition;Burges;Data Mining and Knowledge Discovery,1998

4. The application of parametric multichannel spectral estimates in the study of electrical brain activity;Franaszczuk;Biological Cybernetics,1985

5. Driver drowsiness detection with eyelid related parameters by support vector machine;Hu;Expert System with Applications,2009

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