QUANTIFICATION OF MENTAL STRESS USING COMPLEXITY ANALYSIS OF EEG SIGNALS

Abstract

Detection of mental stress has been receiving great attention from the researchers for many years. Many studies have analyzed electroencephalogram signals in order to estimate mental stress using linear methods. In this paper, a novel nonlinear stress assessment method based on multivariate multiscale entropy has been introduced. Since the multivariate multiscale entropy method characterizes the complexity of nonlinear time series, this research determines the mental stress of human during cognitive workload using complexity of electroencephalogram (EEG) signals. To perform this work, 36 subjects including 9 men and 27 women were participated in the cognitive workload experiment. Multivariate multiscale entropy method has been applied to electroencephalogram data collected from those subjects for estimating mental stress in terms of complexity. The complexity feature of brain electroencephalogram signals collected during resting and cognitive workload has shown statistically significant ([Formula: see text]) differences across brain regions and mental tasks which can be implemented practically for building stress detection system. In addition, the complexity profile of electroencephalogram signals has shown that higher stress is reflected in good counting compared to bad counting. Moreover, the support vector machine (SVM) has shown promising classification between resting and mental counting states by providing 80% sensitivity, 100% specificity and 90% classification accuracy.