Novel approach for stress recognition using EEG signal by SVM classifier

In this work, real time stress level recognition from Electroencephalogram (EEG) signal is proposed. Stress is a mental condition that effects the brain electrical activity to be different from the normal state. A system is designed to classify three levels of stress; Low Stress, Moderate Stress and High Stress. Validation of algorithm is carried out using by Stroop color-word test as stimuli to induce various levels of stress. EEG signals are collected using EEG reusable electrodes, which are placed on forehead scalp during the experiment. Measure of stress is taken by means of questionnaires method. Discrete Wavelet Transform (DWT) is used for pre-processing and using Support Vector Machine (SVM) as the classifier. Further analysis of stress is done by using trained algorithm as reference. Signals collected are continually given to algorithm to verify stress, three levels of stress can be recognized. Stress levels of the person under test are seen on screen using graph.