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Network Biology, 2018, 8(1): 25-34
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Article

EEG-metric based mental stress detection

Gaurav1, R. S. Anand1, Vinod Kumar2
1Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
2Jaypee University of Information Technology, Waknaghat 712100, India

Received 13 October 2017;Accepted 20 December 2017;Published 1 March 2018
IAEES

Abstract
Mental stress level is a vital parameter affecting physical well-being, cognition, emotions, and professional efficiency. With growing adversities in modern living standards, causing abnormal mental stress, it is necessary to measure to cure it. Regular personal stress profile generated can be used as neurofeedback for the clinical as well as personal assessment. This paper describes a method to detect mental stress level based on physiological parameters. In this method, an electroencephalogram (EEG)-metric parameters based binary and ternary stress classifier is developed. This is validated through probabilistic stress profiler of differential stress inventory (a questionnaire based evaluation). Nine channel EEG is used to extract physiological signal. EEG-metric based cognitive state and workload outputs are generated for 41 healthy volunteers (37 males and 4 females, age; 24¡À5 years). All subjects were guided to perform three simple tasks of closed eye, focusing vision on a red dot on center of dark screen and focusing on a white screen. Central tendencies (mean, median and mode) and standard deviation were extracted of EEG-metric (sleep onset, distraction, low engagement, high engagement and cognitive states) as features. Either of the two or three classes of stress are evaluated from probabilistic stress profiler of differential stress inventory and used as training output classes. A supervisory training of multiple layer perceptron based binary support vector machine classifier was used to detect stress class one by one. 40 subject's samples were used for training and interchanging one-by one 41th subjects stress class is determined from the designed classifier. Out of 41 subjects, stress level of 30 subjects is correctly identified by binary classifier and stress level of 26 subjects is correctly identified by ternary classifier, using multi-layer perceptron kernel based SVM.

Keywords EEG-metric;differential stress inventory questionnaire;SVM-MLP.


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