Identification of different frequency sound response from EEG signal

In recent years, a lot of research has been carried out to study on human brain response when listening to different kinds of music, as well as different quality of sound waves. High frequency component and low frequency component, either audible or inaudible, contained in a sound wave is proven to...

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Main Author: Koh, Alice Chee
Format: Thesis
Language:English
Published: 2009
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Online Access:http://eprints.utm.my/id/eprint/18334/1/AliceKohCheeMFKE2009.pdf
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spelling my-utm-ep.183342018-06-25T09:01:50Z Identification of different frequency sound response from EEG signal 2009 Koh, Alice Chee TK Electrical engineering. Electronics Nuclear engineering In recent years, a lot of research has been carried out to study on human brain response when listening to different kinds of music, as well as different quality of sound waves. High frequency component and low frequency component, either audible or inaudible, contained in a sound wave is proven to as a cognitive factor to human acoustic system and can be shown through human brain signal. Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this project, electrical activity of human brain due to sound waves of different frequency is studied based on EEG signals. Collection of EEG signals from 5 healthy adults is done using Neurofax EEG-9100 device. Subjects are exposed to different frequency sound waves of 40 Hz, 500 Hz, 5000 Hz and 15000 Hz. The EEG signals are then processed using signal processing algorithms in Matlab, i.e. Principle Component Analysis, Discrete Wavelet Transform, Fast Fourier Transform etc. Useful information is extracted from the processing of EEG signal, and algorithm to identify the different frequency sound response from the EEG signals is developed using artificial intelligent techniques, i.e. neural network, fuzzy logic, and adaptive neuro-fuzzy system. The result from this project has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and yet different frequency sound response from EEG signal can be identified by using suitable A.I. algorithms. 2009 Thesis http://eprints.utm.my/id/eprint/18334/ http://eprints.utm.my/id/eprint/18334/1/AliceKohCheeMFKE2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic TK Electrical engineering
Electronics Nuclear engineering
spellingShingle TK Electrical engineering
Electronics Nuclear engineering
Koh, Alice Chee
Identification of different frequency sound response from EEG signal
description In recent years, a lot of research has been carried out to study on human brain response when listening to different kinds of music, as well as different quality of sound waves. High frequency component and low frequency component, either audible or inaudible, contained in a sound wave is proven to as a cognitive factor to human acoustic system and can be shown through human brain signal. Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this project, electrical activity of human brain due to sound waves of different frequency is studied based on EEG signals. Collection of EEG signals from 5 healthy adults is done using Neurofax EEG-9100 device. Subjects are exposed to different frequency sound waves of 40 Hz, 500 Hz, 5000 Hz and 15000 Hz. The EEG signals are then processed using signal processing algorithms in Matlab, i.e. Principle Component Analysis, Discrete Wavelet Transform, Fast Fourier Transform etc. Useful information is extracted from the processing of EEG signal, and algorithm to identify the different frequency sound response from the EEG signals is developed using artificial intelligent techniques, i.e. neural network, fuzzy logic, and adaptive neuro-fuzzy system. The result from this project has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and yet different frequency sound response from EEG signal can be identified by using suitable A.I. algorithms.
format Thesis
qualification_level Master's degree
author Koh, Alice Chee
author_facet Koh, Alice Chee
author_sort Koh, Alice Chee
title Identification of different frequency sound response from EEG signal
title_short Identification of different frequency sound response from EEG signal
title_full Identification of different frequency sound response from EEG signal
title_fullStr Identification of different frequency sound response from EEG signal
title_full_unstemmed Identification of different frequency sound response from EEG signal
title_sort identification of different frequency sound response from eeg signal
granting_institution Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department Faculty of Electrical Engineering
publishDate 2009
url http://eprints.utm.my/id/eprint/18334/1/AliceKohCheeMFKE2009.pdf
_version_ 1747815249604509696