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Complexity and entropy analysis to improve gender identification from emotional-based EEGs

Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (a...

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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: Hindawi Publishing 2021
Online Access:http://psasir.upm.edu.my/id/eprint/96484/
https://www.hindawi.com/journals/jhe/2021/8537000/
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spelling my.upm.eprints.964842023-02-08T04:49:11Z http://psasir.upm.edu.my/id/eprint/96484/ Complexity and entropy analysis to improve gender identification from 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 becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent and amplitude-aware permutation entropy features were extracted from the EEG dataset. -nearest neighbors and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate as a complexity feature and as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new hybrid feature fusion method towards developing the novel gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of and features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain. Hindawi Publishing 2021 Article PeerReviewed Al-Qazzaz, Noor Kamal and Sabir, Mohannad K. and Mohd Ali, Sawal Hamid and Ahmad, Siti Anom and Grammer, Karl (2021) Complexity and entropy analysis to improve gender identification from emotional-based EEGs. Journal of Healthcare Engineering, 2021. pp. 1-17. ISSN 2040-2295; ESSN: 2040-2309 https://www.hindawi.com/journals/jhe/2021/8537000/ 10.1155/2021/8537000
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 becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent and amplitude-aware permutation entropy features were extracted from the EEG dataset. -nearest neighbors and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate as a complexity feature and as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new hybrid feature fusion method towards developing the novel gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of and features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain.
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
Complexity and entropy analysis to improve gender identification from 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 Complexity and entropy analysis to improve gender identification from emotional-based EEGs
title_short Complexity and entropy analysis to improve gender identification from emotional-based EEGs
title_full Complexity and entropy analysis to improve gender identification from emotional-based EEGs
title_fullStr Complexity and entropy analysis to improve gender identification from emotional-based EEGs
title_full_unstemmed Complexity and entropy analysis to improve gender identification from emotional-based EEGs
title_sort complexity and entropy analysis to improve gender identification from emotional-based eegs
publisher Hindawi Publishing
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/96484/
https://www.hindawi.com/journals/jhe/2021/8537000/
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score 13.211869

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