Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm

Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals provide unique features that can be considered as user identification techniques. But, it is a challenging task where there are three important things must be addressed carefully in any EEG-base...

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Main Author:	Yahya Alyasseri, Zaid Abdi Alkareem
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/52571/1/ZAID%20ABDI%20ALKAREEM%20YAHYA%20AL%20YASSERI%20-%20TESIS-2.pdf%20cut.pdf
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id	my-usm-ep.52571
record_format	uketd_dc
spelling	my-usm-ep.525712022-05-24T01:42:53Z Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm 2020-06 Yahya Alyasseri, Zaid Abdi Alkareem QA1-939 Mathematics Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals provide unique features that can be considered as user identification techniques. But, it is a challenging task where there are three important things must be addressed carefully in any EEG-based person identification. First, one of the significant challenges concerning is a signal acquisition, which is performed by placing several electrodes on a person’s head. However, it is not necessary to put all these electrodes on a persons’ head. Therefore, the most relevant ones for person identification can be identified and then use a smaller number of electrodes. Second, the EEG signals must be processed to obtain efficient EEG features because there are several noises can corrupt the original EEG signal during the recording time. Third, select efficient features that can be extracted from the EEG signal for achieving the highest accuracy rate. For addressing these points, a novel person identification method that is using EEG with multi-level wavelet decomposition and multi-objective flower pollination algorithm is proposed in this thesis. The proposed method is tested using two standard EEG datasets, namely, Kiern’s and Motor Movement/Imagery. 2020-06 Thesis http://eprints.usm.my/52571/ http://eprints.usm.my/52571/1/ZAID%20ABDI%20ALKAREEM%20YAHYA%20AL%20YASSERI%20-%20TESIS-2.pdf%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer (School of Computer Sciences)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Yahya Alyasseri, Zaid Abdi Alkareem Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
description	Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals provide unique features that can be considered as user identification techniques. But, it is a challenging task where there are three important things must be addressed carefully in any EEG-based person identification. First, one of the significant challenges concerning is a signal acquisition, which is performed by placing several electrodes on a person’s head. However, it is not necessary to put all these electrodes on a persons’ head. Therefore, the most relevant ones for person identification can be identified and then use a smaller number of electrodes. Second, the EEG signals must be processed to obtain efficient EEG features because there are several noises can corrupt the original EEG signal during the recording time. Third, select efficient features that can be extracted from the EEG signal for achieving the highest accuracy rate. For addressing these points, a novel person identification method that is using EEG with multi-level wavelet decomposition and multi-objective flower pollination algorithm is proposed in this thesis. The proposed method is tested using two standard EEG datasets, namely, Kiern’s and Motor Movement/Imagery.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Yahya Alyasseri, Zaid Abdi Alkareem
author_facet	Yahya Alyasseri, Zaid Abdi Alkareem
author_sort	Yahya Alyasseri, Zaid Abdi Alkareem
title	Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
title_short	Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
title_full	Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
title_fullStr	Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
title_full_unstemmed	Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm
title_sort	eeg-based person identification using multi-levelwavelet decomposition with multi-objective flower pollination algorithm
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer (School of Computer Sciences)
publishDate	2020
url	http://eprints.usm.my/52571/1/ZAID%20ABDI%20ALKAREEM%20YAHYA%20AL%20YASSERI%20-%20TESIS-2.pdf%20cut.pdf
_version_	1747822192729522176

Eeg-Based Person Identification Using Multi-Levelwavelet Decomposition With Multi-Objective Flower Pollination Algorithm

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