Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/184072
Title: BCI-driven neurorehabilitation system for training attention and motor skills
Authors: Kenneth, Rhea
Keywords: Computer and Information Science
Issue Date: 2025
Publisher: Nanyang Technological University
Source: Kenneth, R. (2025). BCI-driven neurorehabilitation system for training attention and motor skills. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/184072
Project: CCDS24-0631
Abstract: Brain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have emerged as a promising tool in the rehabilitation of individuals with cognitive and motor deficits. Gamified EEG-BCI systems further benefit rehabilitation by enabling users to interact with a controlled virtual environment through brain activity, offering a non-invasive and engaging method for neurorehabilitation. This paper presents the development of a gamified EEG-BCI cognitive and motor rehabilitation system using Unity, integrating real-time neural signal processing to create an immersive and interactive experience. Leveraging Unity’s flexibility and state-of-the-art ActiChamp+ EEG technology, the game provides immersive challenges that enhance neuroplasticity in the targeted neural signals while enhancing motivation and engagement. The goal of this training system is to strengthen sustained attention and improve hand opening and closing movements in stroke patients with cognitive and motor deficits. This is achieved through a series of tasks that require users to maintain focus and imagine hand open and close movements, reinforcing both cognitive engagement and motor intent. This report outlines the methodologies, theoretical frameworks, and empirical analysis that shaped the development of the program. It also evaluates key outcomes from its application, highlighting both its practical benefits and limitations. Through this examination, the report reinforces the significance of the attention training program, presenting it as a promising approach for cognitive and motor rehabilitation using BCIs.
URI: https://hdl.handle.net/10356/184072
Schools: College of Computing and Data Science 
Research Centres: Computational Intelligence Lab 
Fulltext Permission: embargo_restricted_20260601
Fulltext Availability: With Fulltext
Appears in Collections:CCDS Student Reports (FYP/IA/PA/PI)

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