Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network

Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network

There is a growing interest of research being conducted on detecting eye blink to assist physically impaired people for verbal communication and controlling devices using electroencephalogram (EEG) signal. One particular eye blink can be determined from use of peak points. Therefore, the purpose of...

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Main Authors: Asrul, Adam, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai, Marizan, Mubin
Format: Article
Language:English
English
Published: Czech Technical University, Prague 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/1/Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg%20For%20Signal%20Peak%20Detection%20Using%20Artificial%20Neural%20Network.pdf
http://umpir.ump.edu.my/id/eprint/14721/7/fkee-2016-zuwairie-Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg.pdf
id ump-14721
recordtype eprints
spelling ump-147212018-02-08T00:27:04Z http://umpir.ump.edu.my/id/eprint/14721/ Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network Asrul, Adam Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Marizan, Mubin TK Electrical engineering. Electronics Nuclear engineering There is a growing interest of research being conducted on detecting eye blink to assist physically impaired people for verbal communication and controlling devices using electroencephalogram (EEG) signal. One particular eye blink can be determined from use of peak points. Therefore, the purpose of peak detection algorithm is to distinguish an actual peak location from a list of peak candidates. The need of a good peak model is important in ensuring a satisfy classification performance. In general, there are various peak models available in literature, which have been tested in several peak detection algorithms. In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models. To generalize the performance evaluation, two case studies of eye blink EEG signal are considered, which are single and double eye blink signals. It has been observed that the best test performance, in average, is 91.94% and 87.47% for single and double eye blink signals, respectively. These results indicate that the Acir’s peak model offers high accuracy of peak detection for the two eye blink EEG signals as compared to other peak models. The result of statistical analysis also indicates that the Acir’s peak model is better than Dingle’s and Dumpala’s peak models. Czech Technical University, Prague 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14721/1/Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg%20For%20Signal%20Peak%20Detection%20Using%20Artificial%20Neural%20Network.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/14721/7/fkee-2016-zuwairie-Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg.pdf Asrul, Adam and Zuwairie, Ibrahim and Mohd Ibrahim, Shapiai and Marizan, Mubin (2016) Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network. Neural Network World, 1. pp. 67-89. ISSN 1210-0552 http://dx.doi.org/10.14311/nnw.2016.26.004 DOI: 10.14311/NNW.2016.26.004
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Asrul, Adam
Zuwairie, Ibrahim
Mohd Ibrahim, Shapiai
Marizan, Mubin
Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
description There is a growing interest of research being conducted on detecting eye blink to assist physically impaired people for verbal communication and controlling devices using electroencephalogram (EEG) signal. One particular eye blink can be determined from use of peak points. Therefore, the purpose of peak detection algorithm is to distinguish an actual peak location from a list of peak candidates. The need of a good peak model is important in ensuring a satisfy classification performance. In general, there are various peak models available in literature, which have been tested in several peak detection algorithms. In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpala’s, Acir’s, Liu’s, and Dingle’s peak models. To generalize the performance evaluation, two case studies of eye blink EEG signal are considered, which are single and double eye blink signals. It has been observed that the best test performance, in average, is 91.94% and 87.47% for single and double eye blink signals, respectively. These results indicate that the Acir’s peak model offers high accuracy of peak detection for the two eye blink EEG signals as compared to other peak models. The result of statistical analysis also indicates that the Acir’s peak model is better than Dingle’s and Dumpala’s peak models.
format Article
author Asrul, Adam
Zuwairie, Ibrahim
Mohd Ibrahim, Shapiai
Marizan, Mubin
author_facet Asrul, Adam
Zuwairie, Ibrahim
Mohd Ibrahim, Shapiai
Marizan, Mubin
author_sort Asrul, Adam
title Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
title_short Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
title_full Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
title_fullStr Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
title_full_unstemmed Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network
title_sort evaluation of different peak models of eye blink eeg for signal peak detection using artificial neural network
publisher Czech Technical University, Prague
publishDate 2016
url http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/
http://umpir.ump.edu.my/id/eprint/14721/1/Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg%20For%20Signal%20Peak%20Detection%20Using%20Artificial%20Neural%20Network.pdf
http://umpir.ump.edu.my/id/eprint/14721/7/fkee-2016-zuwairie-Evaluation%20Of%20Different%20Peak%20Models%20Of%20Eye%20Blink%20Eeg.pdf
first_indexed 2023-09-18T22:18:46Z
last_indexed 2023-09-18T22:18:46Z
_version_ 1777415515755511808

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