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Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition

Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find...

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Main Authors: Al-Qammaz, Abdullah Yousef, Ahmad, Farzana Kabir, Yusof, Yuhanis
Format: Article
Language:English
Published: Science Publishing Corporation Inc 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/25274/1/IJET%207%202.15%202018%20148%20149.pdf
http://repo.uum.edu.my/25274/
http://doi.org/10.14419/ijet.v7i2.15.11373
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spelling my.uum.repo.252742018-12-11T01:13:20Z http://repo.uum.edu.my/25274/ Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition Al-Qammaz, Abdullah Yousef Ahmad, Farzana Kabir Yusof, Yuhanis QA75 Electronic computers. Computer science Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively. Science Publishing Corporation Inc 2018 Article PeerReviewed application/pdf en cc_by http://repo.uum.edu.my/25274/1/IJET%207%202.15%202018%20148%20149.pdf Al-Qammaz, Abdullah Yousef and Ahmad, Farzana Kabir and Yusof, Yuhanis (2018) Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition. International Journal of Engineering & Technology, 7 (2.15). pp. 146-149. ISSN 2227-524X http://doi.org/10.14419/ijet.v7i2.15.11373 doi:10.14419/ijet.v7i2.15.11373
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Al-Qammaz, Abdullah Yousef
Ahmad, Farzana Kabir
Yusof, Yuhanis
Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
description Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51±7.17 and 60.97±8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively.
format Article
author Al-Qammaz, Abdullah Yousef
Ahmad, Farzana Kabir
Yusof, Yuhanis
author_facet Al-Qammaz, Abdullah Yousef
Ahmad, Farzana Kabir
Yusof, Yuhanis
author_sort Al-Qammaz, Abdullah Yousef
title Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
title_short Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
title_full Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
title_fullStr Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
title_full_unstemmed Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
title_sort social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
publisher Science Publishing Corporation Inc
publishDate 2018
url http://repo.uum.edu.my/25274/1/IJET%207%202.15%202018%20148%20149.pdf
http://repo.uum.edu.my/25274/
http://doi.org/10.14419/ijet.v7i2.15.11373
_version_ 1644284277334999040
score 13.211869

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