Low-cost wireless electrooculography system for brain-computer interface applications

While communication is an innate ability to most of us, individuals with severe motor disability such as amyotrophic lateral sclerosis (ALS) have difficulty in daily communication. Nowadays, with the help of human-computer interface (HCI), they can communicate using bio-signal which can be measured...

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Main Author: Hoi, Ka Hou
Other Authors: Smitha Kavallur Pisharath Gopi
Format: Final Year Project
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/74171
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-741712023-03-03T20:37:35Z Low-cost wireless electrooculography system for brain-computer interface applications Hoi, Ka Hou Smitha Kavallur Pisharath Gopi School of Computer Science and Engineering DRNTU::Engineering While communication is an innate ability to most of us, individuals with severe motor disability such as amyotrophic lateral sclerosis (ALS) have difficulty in daily communication. Nowadays, with the help of human-computer interface (HCI), they can communicate using bio-signal which can be measured from the human body and monitored. Electrooculography (EOG) is one type of bio-signal, it can be measured as the electrical potential difference generated by eye movement. This project aims to develop a low-cost EOG based speller. The EOG signal acquisition used 5 surface electrodes, which enable the capture of horizontal and vertical eye movement. An EOG signal acquisition circuit was designed and implemented. It consists of amplifiers and filters to condition the signal. Filters were used to filter out unwanted noises from the environment, power line (50Hz) and from the human body. The EOG signal ranges between 15 microvolts to 200 microvolts, hence, amplifier is needed to amplify the signal to a usable range. The horizontal and vertical EOG signal is digitised by the Arduino. A real-time EOG classifying algorithm was developed on the Arduino to classify the signal. The algorithm can classify up to 10 different types of eye movement. This classified information is transmitted wirelessly to a computer. A standard qwerty keyboard and a self-design keyboard were developed using Java to respond to the classified signal. A test was conducted on eight subjects to compare the accuracy and efficiency of both keyboards. It was observed that the self-design has higher performance as compared to the standard qwerty keyboard. The results from the test were also compared with other research, some research featured a commercially available EOG device and some research featured a self-design EOG acquisition circuit. Bachelor of Engineering (Computer Engineering) 2018-05-02T13:38:56Z 2018-05-02T13:38:56Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74171 en Nanyang Technological University 82 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Hoi, Ka Hou
Low-cost wireless electrooculography system for brain-computer interface applications
description While communication is an innate ability to most of us, individuals with severe motor disability such as amyotrophic lateral sclerosis (ALS) have difficulty in daily communication. Nowadays, with the help of human-computer interface (HCI), they can communicate using bio-signal which can be measured from the human body and monitored. Electrooculography (EOG) is one type of bio-signal, it can be measured as the electrical potential difference generated by eye movement. This project aims to develop a low-cost EOG based speller. The EOG signal acquisition used 5 surface electrodes, which enable the capture of horizontal and vertical eye movement. An EOG signal acquisition circuit was designed and implemented. It consists of amplifiers and filters to condition the signal. Filters were used to filter out unwanted noises from the environment, power line (50Hz) and from the human body. The EOG signal ranges between 15 microvolts to 200 microvolts, hence, amplifier is needed to amplify the signal to a usable range. The horizontal and vertical EOG signal is digitised by the Arduino. A real-time EOG classifying algorithm was developed on the Arduino to classify the signal. The algorithm can classify up to 10 different types of eye movement. This classified information is transmitted wirelessly to a computer. A standard qwerty keyboard and a self-design keyboard were developed using Java to respond to the classified signal. A test was conducted on eight subjects to compare the accuracy and efficiency of both keyboards. It was observed that the self-design has higher performance as compared to the standard qwerty keyboard. The results from the test were also compared with other research, some research featured a commercially available EOG device and some research featured a self-design EOG acquisition circuit.
author2 Smitha Kavallur Pisharath Gopi
author_facet Smitha Kavallur Pisharath Gopi
Hoi, Ka Hou
format Final Year Project
author Hoi, Ka Hou
author_sort Hoi, Ka Hou
title Low-cost wireless electrooculography system for brain-computer interface applications
title_short Low-cost wireless electrooculography system for brain-computer interface applications
title_full Low-cost wireless electrooculography system for brain-computer interface applications
title_fullStr Low-cost wireless electrooculography system for brain-computer interface applications
title_full_unstemmed Low-cost wireless electrooculography system for brain-computer interface applications
title_sort low-cost wireless electrooculography system for brain-computer interface applications
publishDate 2018
url http://hdl.handle.net/10356/74171
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