AI-Driven Emotion Recognition Meets EEG for Next-Generation Mental Well-Being
Year: 2025
Type: preprint
Abstract: 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.
Source: Research Square
Authors: Jyoti Mudkanna Gavhane, Gayatri Shirke, Shrushti Modak, Jayashree Prasad, Sathyanarayanan Raman
Institution: MIT Art, Design and Technology University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408317156