The Electroencephalography Signals Using Artificial Neural Network for Monitoring Fatigue System-Reference-Cited by-同舟云学术

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The Electroencephalography Signals Using Artificial Neural Network for Monitoring Fatigue System

Published:2017 Issue: Volume: Page:160-169
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Yimyam Worawut,Ketcham Mahasak

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-60675-0_14

Reference24 articles.

1. Sicard, B.: Risk propensity assessment in military special operations. Mil. Med. 166(10), 871 (2001)

2. Lin, C.T., Ko, L.W., Chung, I.F., Huang, T.Y., Chen, Y.C., Jung, T.P., Liang, S.F.: Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks. IEEE Trans. Circuits Syst. 53(11), 2469–2476 (2006)

3. Department of the Army: Leaders’ Manual for Combat Stress Control, FM22-51, Washington DC, September 1994

4. Edwards, D.J., Sirois, B., Dawson, T., Aguirre, A., et al.: Evaluation of fatigue management technologies using weighted feature matrix method. In: Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Stevenson, Washington (2007)

5. Cai, H., Lin, Y.: An experiment to non-intrusively collect physiological parameters towards driver state detection. In: Proceedings of SAE 2007 World Congress, No. 2007-01-0403. SAE Technical Paper (2007)

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