Synthesized quantitative assessment of human mental fatigue with EEG and HRV

Qingpeng Han; Li Wang; Ping Wang; Bangchun Wen

doi:10.1117/12.664237

21 March 2006 Synthesized quantitative assessment of human mental fatigue with EEG and HRV

Qingpeng Han, Li Wang, Ping Wang, Bangchun Wen

Proceedings Volume 6040, ICMIT 2005: Mechatronics, MEMS, and Smart Materials; 60401V (2006) https://doi.org/10.1117/12.664237
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

The electroencephalograph (EEG) signals and heart rate variable (HRV) signals, which are relative to human body mental stress, are analyzed with the nonlinear dynamics and chaos. Based on calculated three nonlinear parameters, a synthesized quantitative criterion is proposed to assess the body's mental fatigue states. Firstly, the HRV and α wave of EEG from original signals are extracted based on wavelet transform technique. Then, the Largest Lyapunov Exponents, Complexity and Approximate Entropy, are calculated for both HRV and α wave. The three nonlinear parameters reflect quantitatively human physiological activities and can be used to evaluate the mental workload degree. Based on the computation and statistical analysis of practical EEG and HRV data, a synthesized quantitative assessment criterion is induced for mental fatigues with three nonlinear parameters of the above two rhythms. For the known 10 measured data of EEG and HRV signals, the assessment results are obtained with the above laws for different metal fatigue states. To compare with the practical cases, the identification accuracy of mental fatigue or not is up to 100 percent. Furthermore, the accuracies of weak fatigue, middle fatigue and serious fatigue mental workload are all relatively higher; they are about 94.44, 88.89, and 83.33 percent, respectively.

Citation Download Citation

Qingpeng Han, Li Wang, Ping Wang, and Bangchun Wen "Synthesized quantitative assessment of human mental fatigue with EEG and HRV", Proc. SPIE 6040, ICMIT 2005: Mechatronics, MEMS, and Smart Materials, 60401V (21 March 2006); https://doi.org/10.1117/12.664237

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