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Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM


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 the maximum 88%. Hence KPCA-HMM could be a promising model for the estimation of mental fatigue.

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Correspondence to Chongxun Zheng.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, C., Zheng, C., Yu, X. et al. Estimating VDT Mental Fatigue Using Multichannel Linear Descriptors and KPCA-HMM. EURASIP J. Adv. Signal Process. 2008, 185638 (2008).

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  • Feature Vector
  • Heart Rate Variability
  • Hide Markov Model
  • Autonomic Nervous System
  • Spectral Index