Personalized Music Recommendation System for Athletes Using EEG Signals:

B. Madhubala Hindusthan College of Arts and Science, India https://orcid.org/0009-0000-7966-7411 A. V. Senthil Kumar Hindusthan College of Arts and Science, India https://orcid.org/0000-0002-8587-7017 Kyla L. Tennin United Nations Peace Ambassador Foundation, USA https://orcid.org/0000-0002-9849-1316 R. V. Suganya Vels Institute of Science, Technology, and Advanced Studies, India Gaganpreet Kaur Chitkara University, India https://orcid.org/0000-0002-3322-1315 Asadi Srinivasulu Indian Institute of Information Technology, Allahabad, India https://orcid.org/0009-0003-2731-6072 Shadi R. Masadeh Isra University, Jordan Shanmugasundaram Hariharan Vardhaman College of Engineering, India https://orcid.org/0000-0001-9686-4329 Sowjanya Ramisetty The ICFAI Foundation for Higher Education, India https://orcid.org/0009-0004-6455-5248 Sajad Ahmad Mir Rayat Bahra University, India https://orcid.org/0000-0001-6887-4925 Indrarini Dyah Irawati Telcom University, Indonesia Personalized Music Recommendation System for Athletes Using EEG Signals

Personalized music recommendation system that uses EEG data to assess athletes' emotional states, such as calm, focus, excitement, and suggests music to enhance their performance. The system includes three key components: emotion classification, music profiling, and integration. EEG signals are collected from athletes during tasks designed to evoke specific emotions, followed by feature extraction and emotion classification using machine learning. Acurated music database categorizes tracks by emotional characteristics (e.g., tempo, genre, mood) to match identified emotional states. The emotion recognition model integrates with the music recommendation system to suggest real-time tracks. The system is tested using athlete feedback, aiming to improve mental focus, relaxation, and motivation during training and competition, offering a data-driven approach to music therapy for peak performance
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