Classification of vision perception using EEG signals for brain computer interface

Patients suffering from Motor Neuron Disease (MND) and semi-paralysis have trouble to maneuver a conventional wheelchair independently. As a response, this research was conducted whereby an individual’s visual perception can associate to movement controls. The designed system could later on be in...

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Format:	Thesis
Language:	English
Subjects:	Human-computer interaction Visual perception Neuroergonomics Autonomous wheelchair Movement controls
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/4/Eric%20Tiong.pdf
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id	my-unimap-77902
record_format	uketd_dc
spelling	my-unimap-779022023-02-21T07:32:07Z Classification of vision perception using EEG signals for brain computer interface Abdul Hamid, Adom, Prof. Dr. Patients suffering from Motor Neuron Disease (MND) and semi-paralysis have trouble to maneuver a conventional wheelchair independently. As a response, this research was conducted whereby an individual’s visual perception can associate to movement controls. The designed system could later on be integrated into an autonomous wheelchair. The Brain Computer Interface (BCI) system would require the Electroencephalography (EEG) signal to be recorded from the subject using Mindset24 EEG amplifier. Subsequently, the signals’ noise content was been analysed with analysis of variance (ANOVA) whereby signal with high noise content was removed from the samples. Then, spectral energy of different bands of EEG signal (θ, α, β1, β2, β3 and γ) pertaining to an individual’s visual perception were extracted. Next, dimension reduction was performed to select band features based on feature separability using Devijver’s Feature Index (DFI) and Principle Component Analysis (PCA). Finally, neural network models, namely, multi-layered perceptron (MLP), Elman Recurrent Neural Network (ERNN) and nonlinear exogenous autoregressive model (NARX) have been designed to as classifiers to determine the subject’s visual perception, with an average accuracy of over 90%. Among the trained classifier, ERNN was chosen for it yielded a relatively higher performance in the both the Locational Matching and Image Recognition Paradigm in terms of classification accuracies (97.75% and 97.81% respectively). Therefore ERNN is the most suitable classifier to be used for application of visual perception to help MND patient navigate in a wheelchair. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77902 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/1/Page%201-24.pdf 01e12f523360e7c638bb1d840b5723e8 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/2/Full%20text.pdf 380cdc1e382a5aae6101a288c1de9b04 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/4/Eric%20Tiong.pdf 3e28de51e57643eb5b94752211beea0a Universiti Malaysia Perlis (UniMAP) Human-computer interaction Visual perception Neuroergonomics Autonomous wheelchair Movement controls School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Abdul Hamid, Adom, Prof. Dr.
topic	Human-computer interaction Visual perception Neuroergonomics Autonomous wheelchair Movement controls
spellingShingle	Human-computer interaction Visual perception Neuroergonomics Autonomous wheelchair Movement controls Classification of vision perception using EEG signals for brain computer interface
description	Patients suffering from Motor Neuron Disease (MND) and semi-paralysis have trouble to maneuver a conventional wheelchair independently. As a response, this research was conducted whereby an individual’s visual perception can associate to movement controls. The designed system could later on be integrated into an autonomous wheelchair. The Brain Computer Interface (BCI) system would require the Electroencephalography (EEG) signal to be recorded from the subject using Mindset24 EEG amplifier. Subsequently, the signals’ noise content was been analysed with analysis of variance (ANOVA) whereby signal with high noise content was removed from the samples. Then, spectral energy of different bands of EEG signal (θ, α, β1, β2, β3 and γ) pertaining to an individual’s visual perception were extracted. Next, dimension reduction was performed to select band features based on feature separability using Devijver’s Feature Index (DFI) and Principle Component Analysis (PCA). Finally, neural network models, namely, multi-layered perceptron (MLP), Elman Recurrent Neural Network (ERNN) and nonlinear exogenous autoregressive model (NARX) have been designed to as classifiers to determine the subject’s visual perception, with an average accuracy of over 90%. Among the trained classifier, ERNN was chosen for it yielded a relatively higher performance in the both the Locational Matching and Image Recognition Paradigm in terms of classification accuracies (97.75% and 97.81% respectively). Therefore ERNN is the most suitable classifier to be used for application of visual perception to help MND patient navigate in a wheelchair.
format	Thesis
title	Classification of vision perception using EEG signals for brain computer interface
title_short	Classification of vision perception using EEG signals for brain computer interface
title_full	Classification of vision perception using EEG signals for brain computer interface
title_fullStr	Classification of vision perception using EEG signals for brain computer interface
title_full_unstemmed	Classification of vision perception using EEG signals for brain computer interface
title_sort	classification of vision perception using eeg signals for brain computer interface
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77902/4/Eric%20Tiong.pdf
_version_	1776104253343924224

Classification of vision perception using EEG signals for brain computer interface

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