Mental Stress Detection using EEG Signal

World health organization says that, nowadays people mental health problems and also physical problems are because of stress. Stress is originated from brain, neurological signals are important to measure mental stress. 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 uses PNN Algorithm 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 manipulated to detect stress levels using Probabilistic Neural Network (PNN) classifier. For classifying the percentage of stress PNN have been studied. Results have shown the potential of using EEG signal to visualize different levels of stress. The proposed method is useful in developing products for human stress reduction. The success of implementation and development of this research will expected to help in reducing time consumed and human power in determining best solution for stress management.