Ambient assisted living system for amyotrophic lateral sclerosis (ALS) patients

• Main Author: Lyu, Songyu
• Other Authors: Yvonne Lam Ying Hung
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• Online Access: https://hdl.handle.net/10356/157213
• Institution: Nanyang Technological University

This project aims to design and implement a health care system for an individual with chronic disease. The focus of this project is to aid the ALS patients with their communication intention by detecting minor facial movement and/or the relevant EEG signals. Included in this project is a MyndPlay brain band with a single-channel EEG sensor located on the left frontal pole of forehead to record and detect the real-time EEG signal(s) from the end-user, a program has been developed to process the data in order to determine the movement intention of the patient. Based on the classified features, appropriate commands are then generated. The extracted features for this project are selected to be “eyes blinking” and “staring at a color (red) to concentrate”. This is based on the results of my studies on the outstanding effect of eye blinking is reflected in real-time EEG raw signal and also when people stare at a certain color an increasing trend of the attention value in EEG signal is observed. However, the color sensitivity and the strength of intentional blinking are different for different individuals, hence a pre-use characterization is needed for every new user. At the current stage, I use MyndPlay brain band of which locate the electrode is located on the left frontal pole of forehead. It has been set up for recording the experiments data in order to analyze and verify the relationship between EEG signal and the intended features. MATLAB was used to process the data and to establish the relationship. A visual program using OpenCV with designed algorithm has also been developed and integrated into this project. It provides the function to set a personal value on intentional blinking for each user. After that, it translates the extracted features into different commands: “Eyes Blinking” controls the “steed down” and “turning” commands; “Staring at a certain color to concentrate” gives the “start” and “speed up” commands.