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@article{178476,
        author = {Shishir V and Udit Singh and Shivansh Shekhar and S. Narendra Kumar},
        title = {EEG-Based Emotion Recognition Using Fuzzy Logic- A Novel Approach For Mental Health},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4213-4218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178476},
        abstract = {This paper presents a novel approach to emotion recognition using electroencephalogram (EEG) signals through fuzzy logic systems using Python, with specific applications for mental health monitoring in urban environments. Modern urban lifestyles are increasingly associated with chronic stress and related psychological disorders, yet traditional assessment methods rely primarily on subjective self-reporting. We propose a fuzzy logic-based classification system grounded in Russell's circumplex model of affect, which maps emotional states onto a 2- D arousal-valence space. Our approach extracts key EEG features—including frontal alpha asymmetry, beta/alpha ratio, frontal theta, and temporal gamma activity—and maps them to emotional states using adaptive membership functions. The system is validated using the DEAP dataset, demonstrating robust classification accuracy particularly for stress-related emotional states characterized by high arousal and negative valence. Results indicate that the proposed methodology provides an objective, non-invasive means of monitoring emotional states relevant to mental health in urban populations, with potential applications in early stress detection, therapy effectiveness assessment, and personalized mental health interventions.},
        keywords = {EEG signal processing, emotion recognition, fuzzy logic, Russell's circumplex model, mental health monitoring, urban stress.},
        month = {May},
        }