School of Science and Technology 科技學院
Computing Programmes 電腦學系
| Programme | Bachelor of Computing with Honours in Internet Technology Bachelor of Science with Honours in Computer Science |
| Supervisor | Dr. Dani Assi |
| Areas | Intelligent Applications |
| Year of Completion | 2025 |
The aim of the project is to develop a system to monitor brain health by using an EEG-based solution with EEG signal that can be collected by an EEG headset. EEG is commonly employed in the diagnosis and research of various neurological disorders. It is a technique used to record the electrical activity of the brain, which helps identify brainwave patterns that correspond to specific mental tasks or brain function.
Longitudinal Progress Tracking
Monitor changes in cognitive function over time to assess improvement, deterioration, or stability in users' mental health status.
MindWave offers significant benefits for the elderly by providing real-time cognitive health monitoring using EEG technology. This system ensures ongoing brain health assessments, enabling early detection of abnormalities. Personalized healthcare solutions empower elderly users to manage their health proactively.
Short-Term Impact:
Long-Term Impact:
Demonstration Video
Presentation Video
Core Approach
User Registration & Login
Personal Information Management
EEG Data Management
Diagnosis & Analysis
Summary Generation
Personalized Health Recommendations
Figure 1: Use Case Diagram
Figure 2: Component Diagram
System Design
The MindWave application is designed to provide a user-friendly interface for elderly users to monitor their brain health through EEG data analysis. The architecture consists of a front-end user interface and a back end processing system, ensuring seamless interaction and efficient data management.
User-Centric Design:
Modular Architecture:
Security and Privacy:
Scalability:
Back-End System
Flask Back-End:
User Database (PostgreSQL):
EEG Data Handler:
ChatGPT API Bridge:
Figure 3: Operational Diagram
Figure 4: Information-wise Diagram
Figure 5: Structural Diagram
Main page:
Figure 6: Home Section
Login & Registration:
In the login section, users can toggle between login and registration forms. Upon login successful, the user will be able to access the dashboard.
Figure7: Login Form
Figure 8: Registration Form
Dashboard:
Upon logging in, users are presented with the Dashboard, a single‐page interface that centralizes all key functionalities of MindWave. A collapsible sidebar on the left provides one‐click navigation between:
Also, there is a button for AI doctor on the bottom-right corner.
Figure 9: Dashboard
Upload and Diagnosis:
The Upload EEG and Diagnosis Report workflow consists of two main steps:
Figure 10: Upload and Diagnosis
Figure 11: Diagnosis Results
Summary Generation:
Users can obtain a concise, structured overview of their EEG diagnostics through the Summary feature:
Figure 12: Summary Generation
Together, the Summary and AI Doctor modules turn raw EEG data into actionable insights and an ongoing, personalized conversation—helping users to understand their brain health and take informed next steps. | |
AI Doctor:
The AI Doctor provides an interactive, context-aware chat interface that helps you interpret your EEG results and ask follow-up questions:
Seamless Chat Interface
Contextual Brain-Health Expertise
Conversation Memory
Figure 13: AI Docter
Dynamic Follow-Up Suggestions
Markdown-Styled Display
Together, the Summary and AI Doctor modules turn raw EEG data into actionable insights and an ongoing, personalized conversation—helping users to understand their brain health and take informed next steps. | |
MindWave demonstrates strong potential as a digital health solution for elderly cognitive care by integrating:
It effectively addresses cognitive conditions such as Alzheimer's, Parkinson's, dementia, and anxiety, with promising accuracy and a user-friendly dashboard.
Limitations
With these improvements, MindWave could evolve into an even more versatile and impactful cognitive health platform.
Jonathan handles all external affairs include business development, patents write up and public relations. He is frequently interviewed by media and is considered a pioneer in 3D printing products.
After graduating from OUHK, Krutz obtained an M.Sc. in Engineering Management from CityU. He is now completing his second master degree, M.Sc. in Biomedical Engineering, at CUHK. Krutz has a wide range of working experience. He has been with Siemens, VTech, and PCCW.
Hugo Leung Wai-yin, who graduated from his four-year programme in 2015, won the Best Paper Award for his ‘intelligent pill-dispenser’ design at the Institute of Electrical and Electronics Engineering’s International Conference on Consumer Electronics – China 2015.
The pill-dispenser alerts patients via sound and LED flashes to pre-set dosage and time intervals. Unlike units currently on the market, Hugo’s design connects to any mobile phone globally. In explaining how it works, he said: ‘There are three layers in the portable pillbox. The lowest level is a controller with various devices which can be connected to mobile phones in remote locations. Patients are alerted by a sound alarm and flashes. Should they fail to follow their prescribed regime, data can be sent via SMS to relatives and friends for follow up.’ The pill-dispenser has four medicine slots, plus a back-up with a LED alert, topped by a 500ml water bottle. It took Hugo three months of research and coding to complete his design, but he feels it was worth all his time and effort.
Hugo’s public examination results were disappointing and he was at a loss about his future before enrolling at the OUHK, which he now realizes was a major turning point in his life. He is grateful for the OUHK’s learning environment, its industry links and the positive guidance and encouragement from his teachers. The University is now exploring the commercial potential of his design with a pharmaceutical company. He hopes that this will benefit the elderly and chronically ill, as well as the society at large.
Soon after completing his studies, Hugo joined an automation technology company as an assistant engineer. He is responsible for the design and development of automation devices. The target is to minimize human labor and increase the quality of products. He is developing products which are used in various sections, including healthcare, manufacturing and consumer electronics.
| Course Code | Title | Credits | |
|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | |
| COMP S333F | Advanced Programming and AI Algorithms | 5 | |
| COMP S351F | Software Project Management | 5 | |
| COMP S362F | Concurrent and Network Programming | 5 | |
| COMP S363F | Distributed Systems and Parallel Computing | 5 | |
| COMP S382F | Data Mining and Analytics | 5 | |
| COMP S390F | Creative Programming for Games | 5 | |
| COMP S492F | Machine Learning | 5 | |
| ELEC S305F | Computer Networking | 5 | |
| ELEC S348F | IOT Security | 5 | |
| ELEC S371F | Digital Forensics | 5 | |
| ELEC S431F | Blockchain Technologies | 5 | |
| ELEC S425F | Computer and Network Security | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| ELEC S201F | Basic Electronics | 5 | |
| IT S290F | Human Computer Interaction & User Experience Design | 5 | |
| STAT S251F | Statistical Data Analysis | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| COMPS333F | Advanced Programming and AI Algorithms | 5 | |
| COMPS362F | Concurrent and Network Programming | 5 | |
| COMPS363F | Distributed Systems and Parallel Computing | 5 | |
| COMPS380F | Web Applications: Design and Development | 5 | |
| COMPS381F | Server-side Technologies and Cloud Computing | 5 | |
| COMPS382F | Data Mining and Analytics | 5 | |
| COMPS390F | Creative Programming for Games | 5 | |
| COMPS413F | Application Design and Development for Mobile Devices | 5 | |
| COMPS492F | Machine Learning | 5 | |
| ELECS305F | Computer Networking | 5 | |
| ELECS363F | Advanced Computer Design | 5 | |
| ELECS425F | Computer and Network Security | 5 |
