AI-Driven Emotion Recognition Meets EEG for Next-Generation Mental Well-Being

Purpose : The paper addresses the increasing prevalence of mental health challenges, such as stress, anxiety, and depression, highlighting the need for innovative solutions that integrate technology with personalized therapy. It aims to develop a system that provides real-time, individualized mental health support, addressing gaps in traditional therapeutic approaches. Method : The system integrates emotion recognition technology and adaptive therapeutic interventions using EEG signal processing combined with a Linear Discriminant Analysis (LDA) classification model for real-time emotion recognition. It can also be applied in brain-computer interface (BCI) research for emotion classification. Finding : Experimental results and user studies demonstrate the system's potential to improve emotional well-being through tailored, context-aware interventions. Conclusion : The model, which leverages advanced emotion analysis and personalized therapy, is presented as a scalable solution to address global mental health needs and offers an efficient way to classify mental states.