Generative AI and EEG-based Music Personalization for Work Stress Reduction

The escalating prevalence of work-related stress has led to a notable decline in work performance and the mental well-being of the workforce. Studies suggest that personally tailored music can have a positive impact on reducing stress levels. This paper proposes a novel approach to generate personalized music by combining brain activation analysis and musical preference analysis. The proposed method involves three main stages: (1) Source Separation, (2) Brain Activation Analysis, and (3) Personalized Music Generation. In the first stage, we use a variant of the Wave-U-Net to decompose the input song into its vocals and melodies. Subsequently, we employ electroencephalography (EEG) to analyze the user’s brain activation and identify preferred segments. The third stage involves the generation of personalized music using a transformer-based music generation model, which takes into account the user’s music preference and brain activation patterns. Furthermore, this approach was assessed through an experiment with 10 participants, achieving an average satisfaction level of 3.9/5 and a 52.97% increase in Frontal Alpha Asymmetry (FAA) fluctuation percentage after analyzing brain activation with the generated melody. As future work, we intend to conduct intervention-based experiments with large sample sizes and expand into evaluation metrics that provide objective measures of work stress reduction.