Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal

The Dyslexia is learning difficulties which cover reading, spelling and writing. Diagnosis of dyslexia in children at an early stage is very important because they are in the beginning of learning which will help them to cope with the situation very well. An investigation into the feature extraction...

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Main Author:	Che Wan Fadzal, Che Wan Nurul Fatihah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Electric power distribution Electric power transmission Electronics
Online Access:	https://ir.uitm.edu.my/id/eprint/38883/1/38883.pdf
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id	my-uitm-ir.38883
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spelling	my-uitm-ir.388832021-08-27T06:40:01Z Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal 2018-03 Che Wan Fadzal, Che Wan Nurul Fatihah Electric power distribution. Electric power transmission Electronics The Dyslexia is learning difficulties which cover reading, spelling and writing. Diagnosis of dyslexia in children at an early stage is very important because they are in the beginning of learning which will help them to cope with the situation very well. An investigation into the feature extraction of EEG signals with dyslexia using Fast Fourier Transform, Average Spectrum and Welch Power Spectral Density has been studied in this work. Before feature extraction was carried out, the optimum electrode was identified using Fast Fourier Transform. Two types of EEG signals were investigated, one from adults and the other from children. In the first stage, the EEG signals were recorded from 70 adults using electrodes C3, C4, P3, P4, 01 , 02, T3 and FC5. In the second stage, the EEG signals were acquired from 8 normal and 8 dyslexic children using two optimum electrodes found from the first stage. The FFT was then performed on EEG signal from 70 subjects. Then, the EEG signals were analyzed using three methods; Fast Fourier Transform, Average Spectrum and Welch Power Spectral Density from eight subject normal and eight subject dyslexic. Four statistical parameters; minimum frequency, maximum frequency, mean frequency and standard deviation were calculated for each method. From the analysis results, it was found that P3 and P4 are the optimum electrode placement and thus parietal lobe is the active area of the brain during writing. This lobe play an important role in the process related to spatial cognition and in what have been described as quasi- spatial processes, such as used in arithmetic and reading. Therefore, P3 and P4 electrode placements were used in the second stage to identify the best feature extraction method. Results from the second stage showed that the Welch Power Spectral Density is the optimum method to differentiate between normal children with the mean frequency is the optimum parameter. 2018-03 Thesis https://ir.uitm.edu.my/id/eprint/38883/ https://ir.uitm.edu.my/id/eprint/38883/1/38883.pdf text en public masters Universiti Teknologi MARA Faculty of Electrical Engineering Mansor, Wahidah (Assoc. Prof. Datin Dr )
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mansor, Wahidah (Assoc. Prof. Datin Dr )
topic	Electric power distribution Electric power transmission Electronics
spellingShingle	Electric power distribution Electric power transmission Electronics Che Wan Fadzal, Che Wan Nurul Fatihah Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
description	The Dyslexia is learning difficulties which cover reading, spelling and writing. Diagnosis of dyslexia in children at an early stage is very important because they are in the beginning of learning which will help them to cope with the situation very well. An investigation into the feature extraction of EEG signals with dyslexia using Fast Fourier Transform, Average Spectrum and Welch Power Spectral Density has been studied in this work. Before feature extraction was carried out, the optimum electrode was identified using Fast Fourier Transform. Two types of EEG signals were investigated, one from adults and the other from children. In the first stage, the EEG signals were recorded from 70 adults using electrodes C3, C4, P3, P4, 01 , 02, T3 and FC5. In the second stage, the EEG signals were acquired from 8 normal and 8 dyslexic children using two optimum electrodes found from the first stage. The FFT was then performed on EEG signal from 70 subjects. Then, the EEG signals were analyzed using three methods; Fast Fourier Transform, Average Spectrum and Welch Power Spectral Density from eight subject normal and eight subject dyslexic. Four statistical parameters; minimum frequency, maximum frequency, mean frequency and standard deviation were calculated for each method. From the analysis results, it was found that P3 and P4 are the optimum electrode placement and thus parietal lobe is the active area of the brain during writing. This lobe play an important role in the process related to spatial cognition and in what have been described as quasi- spatial processes, such as used in arithmetic and reading. Therefore, P3 and P4 electrode placements were used in the second stage to identify the best feature extraction method. Results from the second stage showed that the Welch Power Spectral Density is the optimum method to differentiate between normal children with the mean frequency is the optimum parameter.
format	Thesis
qualification_level	Master's degree
author	Che Wan Fadzal, Che Wan Nurul Fatihah
author_facet	Che Wan Fadzal, Che Wan Nurul Fatihah
author_sort	Che Wan Fadzal, Che Wan Nurul Fatihah
title	Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
title_short	Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
title_full	Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
title_fullStr	Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
title_full_unstemmed	Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal
title_sort	electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / che wan nurul fatihah che wan fadzal
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	https://ir.uitm.edu.my/id/eprint/38883/1/38883.pdf
_version_	1783734489341493248

Electrode optimisation and feature extraction of electroencephalogram signal to identify dyslexic and normal children / Che Wan Nurul Fatihah Che Wan Fadzal

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