Date of Graduation
12-2015
Document Type
Thesis
Degree Name
Bachelor of Science in Electrical Engineering
Degree Level
Undergraduate
Department
Electrical Engineering
Advisor/Mentor
Li, Baohua
Abstract
This thesis presents the design and experiment of a system that can detect the human thinking such as driving directions and letters using the brainwave signals known as electroencephalogram (EEG) and a machine learning algorithm called support vector machine (SVM). This research is motivated by amyotrophic lateral sclerosis (ALS) disease which makes patients seriously lose mobility and speaking capabilities. The developed system in this thesis has three main steps. First, wearing EPOC headset from Emotiv Company, a user can record the EEG signals when he/she is thinking a direction or a letter, and also save the data in a personal computer wirelessly. Next, a large amount of EEG data carrying the information of different directions and letters from this user are used to train SVM classification model exhaustively. Finally, the well-trained SVM model will be used to detect any new thought about directions and letters from the user. The detection results from the SVM model will be transmitted wirelessly to a robotic car with LCD display built with Arduino microcontrollers to control its motions as well as the alphabetic display on LCD. One of the great potential applications of the developed system is to make an advanced brain control wheel chair system with LCD display for aiding ALS patients with their mobility and daily communications.
Citation
Le, Q. M. (2015). EEG-Controlling Robotic Car and Alphabetic Display by Support Vector Machine for Aiding Amyotrophic Lateral Sclerosis Patients. Electrical Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/eleguht/44
