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@article{185657,
        author = {Dr.Ganesh Gorakhnath Taware and Ms.Tanuja Tukaram Kharat and Ms.Prapti Kailas Gawade and Ms.Gayatri Uttam Sathe and Ms.Kirti Sharnappa Zagge},
        title = {AI–EEG DRIVEN MENTAL WELLNESS PLATFORM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2372-2375},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185657},
        abstract = {The project aims to develop an AI-powered web- based platform for monitoring and improving mental wellness using EEG data. The system leverages machine learning and deep learning classifiers (e.g., CNN, Random Forest) to analyze EEG signals and detect emotional states such as stress, anxiety, calm, and focus. Key technologies include Java full-stack development, Python, HTML/CSS/JavaScript, and AI/ML libraries such as TensorFlow, Keras, and Scikit-learn. The platform integrates NLP-based chatbots, personalized music and meditation rec- ommendations, and mood-tracking dashboards to provide real- time feedback and wellness interventions. Models are trained on labeled EEG datasets and user feedback; evaluation uses metrics like accuracy, precision, recall, and F1-score. This review highlights current techniques, findings, challenges, and future directions for AI–EEG mental wellness platforms.},
        keywords = {EEG, Mental Wellness, Convolutional Neural Network (CNN), Random Forest, NLP, Chatbot, Emotion Recog- nition, AI for Health},
        month = {October},
        }