A Multimodal Approach to Personalized Stress Alleviation: Integrating EEG-Guided Music Recommendation for Enhanced Therapeutic Outcomes

Due to numerous variables, including the covid-19 pandemic, economic unpredictability and climate change, nowadays a significant amount of stress and depression is experienced by individuals. Chronic stress can put your health at higher risk of health concerns. This serious health problem can be addressed by analysing electroencephalogram (EEG) signals and detecting the stress levels. This paper proposes Stress Detection and Music Therapy Application in which Power Spectral Density of each frequency band is extracted from brain wave signals and theta to beta ratio, which highly correlates with stress level, is evaluated. This approach makes use of K-Means Clustering followed by Support Vector Machine (SVM) to classify the stress into 3 levels i.e. stressed, intermediate and not stressed. The relevant music is recommended depending stress level of the individual. The proposed methodology aims to facilitate relaxation for individuals dealing with panic and high-stress scenarios by leveraging the therapeutic benefits of music.