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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) |
Files in This Item:
File | Description | Size | Format | |
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CCDS24_0631_Rhea_Kenneth_FYP.pdf Until 2026-06-01 | 24.97 MB | Adobe PDF | Under embargo until Jun 01, 2026 |
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