EEG-Based Fatigue Classification by Using Parallel Hidden Markov Model and Pattern Classifier Combination
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[1] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[2] Hong Yu,et al. Semi-Supervised Clustering for Vigilance Analysis Based on EEG , 2007, 2007 International Joint Conference on Neural Networks.
[3] Matthew B. Weinger. Vigilance, Boredom, and Sleepiness , 2004, Journal of Clinical Monitoring and Computing.
[4] Bao-Liang Lu,et al. Dynamic clustering for vigilance analysis based on EEG , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[5] Bing Li,et al. Gender classification by combining clothing, hair and facial component classifiers , 2012, Neurocomputing.
[6] Raja Parasuraman,et al. Monitoring an Automated System for a Single Failure: Vigilance and Task Complexity Effects , 1996 .
[7] Michel Grabisch,et al. Fuzzy integral for classification and feature extraction , 2000 .
[8] B. Oken,et al. Vigilance, alertness, or sustained attention: physiological basis and measurement , 2006, Clinical Neurophysiology.
[9] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[10] John H. L. Hansen,et al. Discrete-Time Processing of Speech Signals , 1993 .
[11] Joseph Tabrikian,et al. Classification of multichannel EEG patterns using parallel hidden Markov models , 2012, Medical & Biological Engineering & Computing.
[12] Bao-Liang Lu,et al. Off-line and on-line vigilance estimation based on linear dynamical system and manifold learning , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.