EEG Signal Stress Detection

Health organization shows stress is a significant problem of this generation which affects both physical as well as the mental health of people. According to Research in area of stress detection has improved many techniques for monitoring the human brain and Body which detects Stress. The traditional stress detection system is based on physiological signals and facial expression techniques. This proposes a novel method that detects the stress using EEG signals and reduces the stress by introducing the interventions into the system. Propose methodology delivered system which use SVM Algorithm for divide the subjects into different categories and to measure stress to estimate the stress level. By Result generating throw system humans can take action for determining best solution for stress management. System generates feedback from stress hormones. The collected data was then used to extract a set of features using Discrete Wavelet Transform (DWT). The extracted features are operated to identify anxiety stages using hierarchical Support Vector Machine (SVM) classifier. For categorizing "stressed" and "relaxed" conditions SVM have been planned. Results have shown the possible of using EEG signal to envision different levels of stress. This paper converses the methods and modifications planned earlier in literature for removing entrance from an EEG signal and categorizing them.