A wearable single EEG channel analysis for mental stress state detection

Mental stress is a world's apprising issue due to its impact on health and the economy. Chronic stress negatively affects human cognitive abilities and decision-making. To avoid its serious consequences, it is paramount important to detect it at an early stage. In this study, we assessed the levels of stress on 28 healthy subjects by utilizing an Electroencephalogram (EEG) of a single channel and machine learning approach. The EEG signals were analyzed by extracting 20 features from the time and frequency domains. The optimum features were, then, selected using decision trees of information gain. Consequently, we classified the levels of stress using support vector machines (SVM) classifier with a GRID Search optimizer. The proposed feature selection method results in a 66% reduction of feature vector space and achieved an accuracy of 86% using the optimized SVM classifier. Our result demonstrates the effectiveness of the proposed method for the development of real-life stress applications.