EEG Brain Signals for Limb Disabilities

Lee, Francis Jun Feng (2025) EEG Brain Signals for Limb Disabilities. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

This project explores the development of an innovative prosthetic limb control system for individuals with lower limb disabilities, utilizing EEG brain signals to enhance the functionality, comfort, and quality of life. Traditional prosthetic limbs often face limitations such as discomfort, poor durability, and restricted mobility, which negatively affect the mental well-being and daily life of users. To address these challenges, this research proposes a system that leverages EEG brain signals for real-time control of a 3D model representing prosthetic lower limbs. The system is powered by Convolutional Neural Networks (CNN) for precise classification and interpretation of motor cortex signals related to lower limb movements. Data preprocessing ensures high data quality before training the model, while Unity is used for 3D model rendering and Python for EEG signal processing. An Agile Development Model was employed to ensure flexibility, continuous improvement, and user feedback integration throughout the project. The proposed system aims to improve prosthetic control by offering smoother, more natural limb movements, enhancing user independence. Achievements include the creation of a functional prototype with real-time control of a 3D model, although challenges such as data set limitations and lack of real prosthetic testing were encountered. The project’s contributions include filling gaps in existing prosthetic technology, improving accessibility for users with lower limb disabilities, and offering valuable insights for future developments in healthcare and rehabilitation. Future work will focus on optimizing signal precision, integrating real prosthetics for testing, and enhancing user engagement for a more comprehensive solution.