Electroencephalogram Based Stress Detection Using Machine Learning

This study analyses the topic of stress, highlighting the vital necessity of efficient coping techniques and mentality-based approaches to identify different stress states in a timely manner. The study focuses on the intricate function of electroencephalogram (EEG) signals in detecting stress for medical professionals’ diagnosis so that treatment of disorders linked to stress could be treated at the earliest. The suggested method begins with preprocessing, feature extraction and further proceeds with development of ensemble model. This work investigates the identification of stress using EEG signals. The ensemble model achieves an amazing 98% accuracy on scaled data after undergoing thorough testing. The study emphasizes the critical importance of stress identification and presents ensemble technique for sophisticated EEG-based stress identification. This underscores the importance of stress identification and how it can affect the way a person perceives and handles stress, which will ultimately lead to an improved quality of life.