Fundamentals of speech recognition
Fundamentals of speech recognition
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mining physiological conditions from heart rate variability analysis
IEEE Computational Intelligence Magazine
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
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The impacts of prolonged visual display terminal (VDT) work on central nervous system and autonomic nervous system are observed and analyzed based on electroencephalogram (EEG) and heart rate variability (HRV). Power spectral indices of HRV, the P300 components based on visual oddball task, and multichannel linear descriptors of EEG are combined to estimate the change of mental fatigue. The results show that long-term VDT work induces the mental fatigue. The power spectral of HRV, the P300 components, and multichannel linear descriptors of EEG are correlated with mental fatigue level. The cognitive information processing would come down after long-term VDT work. Moreover, the multichannel linear descriptors of EEG can effectively reflect the changes of θ, α, and β waves and may be used as the indices of the mental fatigue level. The kernel principal component analysis (KPCA) and hidden Markov model (HMM) are combined to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-HMM method can effectively reduce the dimensions of the feature vectors, accelerate the classification speed, and improve the accuracy of mental fatigue to achieve themaximum 88%. Hence KPCA-HMM could be a promising model for the estimation of mental fatigue.