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Spectro-spatial profile for gender identification using emotional-based EEG Signals

Identifying gender has become essential specially to support automatic human-computer interface applications and to customize interactions based on affective responses. The electroencephalogram (EEG) has been adopted for recording the neuronal information as waveforms from the scalp. The objective o...

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Main Authors: Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Md Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl
Format: Article
Language:English
Published: Universiti Tun Hussein Onn Malaysia 2021
Online Access:http://psasir.upm.edu.my/id/eprint/95129/1/Spectro-spatial%20profile%20for%20gender%20identification%20using.pdf
http://psasir.upm.edu.my/id/eprint/95129/
https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/8641
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spelling my.upm.eprints.951292023-01-03T06:21:50Z http://psasir.upm.edu.my/id/eprint/95129/ Spectro-spatial profile for gender identification using emotional-based EEG Signals Al-Qazzaz, Noor Kamal Sabir, Mohannad K. Md Ali, Sawal Hamid Ahmad, Siti Anom Grammer, Karl Identifying gender has become essential specially to support automatic human-computer interface applications and to customize interactions based on affective responses. The electroencephalogram (EEG) has been adopted for recording the neuronal information as waveforms from the scalp. The objective of this study was twofold. First, to identify genders from four different emotional states using spectral relative power biomarkers. Second, to develop Spectro-spatial profiles that afford additional information for gender identification using emotional-based EEGs. The dataset has been collected from ten healthful volunteer students from the University of Vienna while watching short emotional audio-visual clips of angry, happiness, sadness, and neutral emotions. Wavelet (WT) has been used as a denoising technique, the spectral relative power features of delta (), theta (), alpha (), beta () and gamma () were extracted from each recorded EEG channel. In the subsequent steps, analysis of variance (ANOVA) and Pearson’s correlation analysis were performed to characterize the emotional-based EEG biomarkers towards developing the Spectro-spatial profile to identify gender differences. The results show that the spectral set of features may provide and convey reliable biomarkers for identifying Spectro-spatial profiles from four different emotional states. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects and as an intervention on the brain. The results revealed that almost high relative powers from all emotional states appear in females compared to males. Particularly, was the most prominent for anger, and were widely observed in happiness, was the most appears in sadness, and were the powers that appears widely in neutral. Moreover, in females, neut was correlated with and _ang, _neut was mostly correlated with _ang. Besides, _neut was correlated with _ang, _neut was correlated with _ang, _neut was mostly correlated with _sad. Moreover, in males, _neut showed a very strong correlation with _sadness whereas _neut was correlated with _hap and _neut was correlated with _hap. Therefore, the proposed system using the WT denoising method, spectral relative power markers, and the spectro-spatial profile plays a crucial role in characterizing the emotional-based EEGs towards gender identification. The classification results were 89.46% for SVM and 90% for the KNN. Therefore, the proposed system using the WT denoising method, spectral relative powers features, SVM, and KNN classifiers were crucial in gender identification and characterizing the emotional EEG signals. Universiti Tun Hussein Onn Malaysia 2021-05-20 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/95129/1/Spectro-spatial%20profile%20for%20gender%20identification%20using.pdf Al-Qazzaz, Noor Kamal and Sabir, Mohannad K. and Md Ali, Sawal Hamid and Ahmad, Siti Anom and Grammer, Karl (2021) Spectro-spatial profile for gender identification using emotional-based EEG Signals. International Journal of Integrated Engineering, 13 (5). 67 - 78. ISSN 2229-838X; ESSN: 2600-7916 https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/8641 10.30880/ijie.2021.13.05.008
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/
language English
description Identifying gender has become essential specially to support automatic human-computer interface applications and to customize interactions based on affective responses. The electroencephalogram (EEG) has been adopted for recording the neuronal information as waveforms from the scalp. The objective of this study was twofold. First, to identify genders from four different emotional states using spectral relative power biomarkers. Second, to develop Spectro-spatial profiles that afford additional information for gender identification using emotional-based EEGs. The dataset has been collected from ten healthful volunteer students from the University of Vienna while watching short emotional audio-visual clips of angry, happiness, sadness, and neutral emotions. Wavelet (WT) has been used as a denoising technique, the spectral relative power features of delta (), theta (), alpha (), beta () and gamma () were extracted from each recorded EEG channel. In the subsequent steps, analysis of variance (ANOVA) and Pearson’s correlation analysis were performed to characterize the emotional-based EEG biomarkers towards developing the Spectro-spatial profile to identify gender differences. The results show that the spectral set of features may provide and convey reliable biomarkers for identifying Spectro-spatial profiles from four different emotional states. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects and as an intervention on the brain. The results revealed that almost high relative powers from all emotional states appear in females compared to males. Particularly, was the most prominent for anger, and were widely observed in happiness, was the most appears in sadness, and were the powers that appears widely in neutral. Moreover, in females, neut was correlated with and _ang, _neut was mostly correlated with _ang. Besides, _neut was correlated with _ang, _neut was correlated with _ang, _neut was mostly correlated with _sad. Moreover, in males, _neut showed a very strong correlation with _sadness whereas _neut was correlated with _hap and _neut was correlated with _hap. Therefore, the proposed system using the WT denoising method, spectral relative power markers, and the spectro-spatial profile plays a crucial role in characterizing the emotional-based EEGs towards gender identification. The classification results were 89.46% for SVM and 90% for the KNN. Therefore, the proposed system using the WT denoising method, spectral relative powers features, SVM, and KNN classifiers were crucial in gender identification and characterizing the emotional EEG signals.
format Article
author Al-Qazzaz, Noor Kamal
Sabir, Mohannad K.
Md Ali, Sawal Hamid
Ahmad, Siti Anom
Grammer, Karl
spellingShingle Al-Qazzaz, Noor Kamal
Sabir, Mohannad K.
Md Ali, Sawal Hamid
Ahmad, Siti Anom
Grammer, Karl
Spectro-spatial profile for gender identification using emotional-based EEG Signals
author_facet Al-Qazzaz, Noor Kamal
Sabir, Mohannad K.
Md Ali, Sawal Hamid
Ahmad, Siti Anom
Grammer, Karl
author_sort Al-Qazzaz, Noor Kamal
title Spectro-spatial profile for gender identification using emotional-based EEG Signals
title_short Spectro-spatial profile for gender identification using emotional-based EEG Signals
title_full Spectro-spatial profile for gender identification using emotional-based EEG Signals
title_fullStr Spectro-spatial profile for gender identification using emotional-based EEG Signals
title_full_unstemmed Spectro-spatial profile for gender identification using emotional-based EEG Signals
title_sort spectro-spatial profile for gender identification using emotional-based eeg signals
publisher Universiti Tun Hussein Onn Malaysia
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/95129/1/Spectro-spatial%20profile%20for%20gender%20identification%20using.pdf
http://psasir.upm.edu.my/id/eprint/95129/
https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/8641
_version_ 1754531209000517632
score 13.211869

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