Emotional Stress Assessment by Combining Characters of Complexity and Entropy
1 Institute of Biomedical Engineering, Yanshan University, Qinhuangdao 066004, China
2 Measurement Technology and Instrumentation Key Lab of Hebei Province，Qinhuangdao 066004, China
3 Institute of Information Science and Technology, Dalian Polytechnic University, Dalian116000, China
Abstract Pressure in long term may cause some diseases. It is important to assess the state of emotional stress reasonably and effectively. It is a reasonable method of pressure condition assessment based on the EEG (electroencephalograph), because of EEG contains plenty of emotional information. In this paper, focusing on the EEG signal characters extracting of emotional stress, an algorithm was investigated. The degree of random was quantified by the Kc factor. The complexity and energy distributing were quantfied by the approximate entropy and wavelet entropy. The Kc factor, approximate entropy and wavelet entropy were fused as the emotional characters by the optimal support vector machine. Based on principle of the overall optimization and the survival of the fittest of genetic algorithm, selectioncrossovermutation were done to pursue the optical parameters of SVM. “Fruit Ninja” game was selected as a source of stress, and a total of 92 groups of EEG signals were collected from 8 subjects. Assessment results showed that the highest classification accuracy was 94.12%, and average accuracy was 82.06%. The level of sensitivity to the stress was different among the brain regions. The left hemisphere was more sensitive to stress than the right one. The research is expected to be helpful for people to take proper methods of relieving stress and restoring physical and mental health.
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