Binary answering machine using EEG brain signals

The human brain is a highly complex and important organ that is centre of the human nervous system. The brain is also the control centre for physical movement, sleep, hunger, thirst, and virtually every other vital activity necessary to survive. The presence of electrical current in the brain was fi...

Full description

Saved in:
Bibliographic Details
Main Author: Chen, Wei Si.
Other Authors: Ser Wee
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/40170
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:The human brain is a highly complex and important organ that is centre of the human nervous system. The brain is also the control centre for physical movement, sleep, hunger, thirst, and virtually every other vital activity necessary to survive. The presence of electrical current in the brain was first discovered by an English physician, Richard Caton, in 1875 and Electroencephalography was later developed by a German scientist Hans Berger in 1924 to record the electrical activity of the brain. Since then, EEG had been used in researches related to the brain. The objective of this project is to design an experiment to investigate EEG brain signals corresponding to different mental states, being concentration and resting, after which the results were analyzed and used to develop a binary answering machine. The background information of the brain and electroencephalography (EEG) would be touched on in this report. Studies had shown that alpha waves of frequency between 8Hz and 13 Hz were related to relaxation and beta waves of frequency between 13Hz and 30Hz were evident during concentration. This information would later shape the design of the experiment. A set of experimental procedure was initiated to capture the relevant EEG signals. The hardware g.tec g.USBamp coupled with a simulink model was used to capture the signals when the subject was in concentration and relaxation mode. Time-domain analysis and frequency-domain analysis were then performed on the collected signals via Matlab. The results were tabulated and observations were noted. Finally, this report would also touch on the limitations and problems faced during the course of this project and some recommendations were made for others who might be doing a similar project in the future.