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Personalized Music Recommendation System for Athletes Using EEG Signals
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Author(s): B. Madhubala (Hindusthan College of Arts and Science, India), A. V. Senthil Kumar (Hindusthan College of Arts and Science, India), Kyla L. Tennin (United Nations Peace Ambassador Foundation, USA), R. V. Suganya (Vels Institute of Science, Technology, and Advanced Studies, India), Gaganpreet Kaur (Chitkara University, India), Asadi Srinivasulu (Indian Institute of Information Technology, Allahabad, India), Shadi R. Masadeh (Isra University, Jordan), Shanmugasundaram Hariharan (Vardhaman College of Engineering, India), Sowjanya Ramisetty (The ICFAI Foundation for Higher Education, India), Sajad Ahmad Mir (Rayat Bahra University, India)and Indrarini Dyah Irawati (Telcom University, Indonesia)
Copyright: 2025
Pages: 28
Source title:
Coaching in Communication Research
Source Author(s)/Editor(s): Çiçek Topçu (Antalya Belek University, Turkey)
DOI: 10.4018/979-8-3693-7959-2.ch007
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Abstract
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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