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Classification of vision perception using EEG signals for brain computer interface

Master of Science in Mechatronic Engineering

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Bibliographic Details
Main Author: Eric Tiong, Kung Woo
Other Authors: Abdul Hamid, Adom, Prof. Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2016
Subjects:
Human-computer interaction
Visual perception
Neuroergonomics
Autonomous wheelchair
Movement controls
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77902
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spelling my.unimap-779022023-02-21T07:32:07Z Classification of vision perception using EEG signals for brain computer interface Eric Tiong, Kung Woo Abdul Hamid, Adom, Prof. Dr. Human-computer interaction Visual perception Neuroergonomics Autonomous wheelchair Movement controls Master of Science in Mechatronic Engineering 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. 2016 2023-02-21T07:28:00Z 2023-02-21T07:28:00Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77902 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Human-computer interaction
Visual perception
Neuroergonomics
Autonomous wheelchair
Movement controls
spellingShingle Human-computer interaction
Visual perception
Neuroergonomics
Autonomous wheelchair
Movement controls
Eric Tiong, Kung Woo
Classification of vision perception using EEG signals for brain computer interface
description Master of Science in Mechatronic Engineering
author2 Abdul Hamid, Adom, Prof. Dr.
author_facet Abdul Hamid, Adom, Prof. Dr.
Eric Tiong, Kung Woo
format Thesis
author Eric Tiong, Kung Woo
author_sort Eric Tiong, Kung Woo
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
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2016
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77902
_version_ 1772813083801026560
score 13.214268

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