EEG Based Stress Classification in Response to Stress Stimulus

Stress, either physical or mental, is experienced by almost every person at some point in his lifetime. Stress is one of the leading causes of various diseases and burdens society globally. Stress badly affects an individual's well-being. Thus, stress-related study is an emerging field, and in the past decade, a lot of attention has been given to the detection and classification of stress. The estimation of stress in the individual helps in stress management before it invades the human mind and body. In this paper, we proposed a system for the detection and classification of stress. We compared the various machine learning algorithms for stress classification using EEG signal recordings. Interaxon Muse device having four dry electrodes has been used for data collection. We have collected the EEG data from 20 subjects. The stress was induced in these volunteers by showing stressful videos to them, and the EEG signal was then acquired. The frequency-domain features such as absolute band powers were extracted from EEG signals. The data were then classified into stress and non-stressed using different machine learning methods - Random Forest, Support Vector Machine, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Gradient Boosting. We performed 10-fold cross-validation, and the average classification accuracy of 95.65% was obtained using the gradient boosting method.