Electroencephalogram Signal Analysis Using Wavelet Transform and Support Vector Machine for Human Stress Recognition

Abstract

The human stress is a mental condition that can abnormally change the brain electrical activity, thus, electroencephalogram (EEG) signal measurements can detect and quantify those brain cognitive changes that are differentiated from the normal state. In this research work, EEG signals are used for the analysis and detection of the level of human stress. The EEG signals are collected from the human being called it as a subject under test. The stroop colour test has been used as a stressor to induce stress in the subjects under test. The various levels of stress in the stroop test have been verified to low, moderate, and high levels of stress in the subject. The input signals are then decomposed into the number of a narrowband signal using wavelet transform. During the experimentation important features are also extracted from EEG signal to identify normal and abnormal signals. The SVM classifier has been used in our research work for the classification of stress and non stress signals. The performance of the proposed system using SVM is comparatively good in dependent and independent systems. The highest accuracy achieved in this study is 90% (Standard Deviation = 0.015) for user-dependent systems and 72.3% (SD = 0.08) for user-independent systems. The results show that the proposed system is reliable for detecting stress and normal levels respectively.