Detection of Mental Stress using EEG signals

Stress is defined as a state of mental or psychological strain or tension resulting from unpleasant or demanding circumstances. These circumstances can be psychological or social. Electroencephalogram (EEG) is a tool to record the electrical activity over the scalp. This technique is widely used in clinical and research setting. In clinical setting, the EEG signal is used to diagnose the disease related to brain. In research setting, the EEG signals are used in rehabilitation; mental stress study. In this paper, detection of stress and identifying of stress levels using electroencephalogram (EEG) analysis in MATLAB using Machine Learning framework is proposed. We discuss several methods that can be used to investigate EEG signals. The features can be obtained via Discrete Cosine Transform and Discrete Wave Transform. The different classifiers that can be used are – Support Vector Machine, Linear Discriminant Analysis, KNearest Neighbour, Artificial Neural Network, Naïve Bayes.