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Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs

Investigating gender differences based on emotional changes supports automatic interpretation of human intentions and preferences. This allows emotion applications to respond better to requirements and customize interactions based on affective responses. The electroencephalogram (EEG) is a tool that...

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Main Authors:	Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl
Format:	Article
Published:	Institute of Electrical and Electronics Engineers 2021
Online Access:	http://psasir.upm.edu.my/id/eprint/94286/ https://ieeexplore.ieee.org/document/9481124
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spelling	my.upm.eprints.942862023-05-08T04:45:01Z http://psasir.upm.edu.my/id/eprint/94286/ Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs Al-Qazzaz, Noor Kamal Sabir, Mohannad K. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Grammer, Karl Investigating gender differences based on emotional changes supports automatic interpretation of human intentions and preferences. This allows emotion applications to respond better to requirements and customize interactions based on affective responses. The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. The main purpose of this paper is twofold. Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. The EEGs of thirty healthy students from the University of Vienna were recorded while they were watched four short video clips depicting the emotions of anger, happiness, sadness and neutral. In this study, the wavelet transform (WT) de-noising technique, linear spectral mean frequency ( meanF) and nonlinear multiscale fuzzy entropy ( MFE) features were used. The individual performance of these attributes was statistically examined using analysis of variance (ANOVA) to represent the gender behavior in the brain-emotion in females and males. Then, these two features were fused into a set of hybrid spectral-entropy attributes ( SEA). Consequently, optimization algorithms including binary gravitation search algorithm (BGSA) and binary particle swarm optimization (BPSO), were employed to identify the optimal channels for gender classification. Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. The results show linear and nonlinear features are remarkable neuromarkers for investigating gender-based differences in emotional states. Moreover, the results show significant enhancement in the overall accuracy of classification achieved by using the BGSA optimization algorithm with the proposed hybrid SEA set when compared to individual features. Therefore, the proposed methods were effective in improving the process of automatic gender recognition from the emotional-based EEG signals. Institute of Electrical and Electronics Engineers 2021-07-12 Article PeerReviewed Al-Qazzaz, Noor Kamal and Sabir, Mohannad K. and Mohd Ali, Sawal Hamid and Ahmad, Siti Anom and Grammer, Karl (2021) Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs. IEEE Access, 9. 107059 - 107078. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9481124 10.1109/ACCESS.2021.3096430
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	Investigating gender differences based on emotional changes supports automatic interpretation of human intentions and preferences. This allows emotion applications to respond better to requirements and customize interactions based on affective responses. The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. The main purpose of this paper is twofold. Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. The EEGs of thirty healthy students from the University of Vienna were recorded while they were watched four short video clips depicting the emotions of anger, happiness, sadness and neutral. In this study, the wavelet transform (WT) de-noising technique, linear spectral mean frequency ( meanF) and nonlinear multiscale fuzzy entropy ( MFE) features were used. The individual performance of these attributes was statistically examined using analysis of variance (ANOVA) to represent the gender behavior in the brain-emotion in females and males. Then, these two features were fused into a set of hybrid spectral-entropy attributes ( SEA). Consequently, optimization algorithms including binary gravitation search algorithm (BGSA) and binary particle swarm optimization (BPSO), were employed to identify the optimal channels for gender classification. Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. The results show linear and nonlinear features are remarkable neuromarkers for investigating gender-based differences in emotional states. Moreover, the results show significant enhancement in the overall accuracy of classification achieved by using the BGSA optimization algorithm with the proposed hybrid SEA set when compared to individual features. Therefore, the proposed methods were effective in improving the process of automatic gender recognition from the emotional-based EEG signals.
format	Article
author	Al-Qazzaz, Noor Kamal Sabir, Mohannad K. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Grammer, Karl
spellingShingle	Al-Qazzaz, Noor Kamal Sabir, Mohannad K. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Grammer, Karl Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
author_facet	Al-Qazzaz, Noor Kamal Sabir, Mohannad K. Mohd Ali, Sawal Hamid Ahmad, Siti Anom Grammer, Karl
author_sort	Al-Qazzaz, Noor Kamal
title	Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
title_short	Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
title_full	Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
title_fullStr	Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
title_full_unstemmed	Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
title_sort	multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based eegs
publisher	Institute of Electrical and Electronics Engineers
publishDate	2021
url	http://psasir.upm.edu.my/id/eprint/94286/ https://ieeexplore.ieee.org/document/9481124
_version_	1768009398230188032
score	13.211869

Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs

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