The Role of Spectral Power Ratio in Characterizing Emotional EEG for Gender Identification

Al-Qazzaz, Noor Kamal; Sabir, Mohannad K.; Ali, Sawal Hamid Md; Ahmad, Siti Anom; Grammer, Karl

doi:10.1109/iecbes48179.2021.9398737

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…However, our results were consented to different investigations, for example Kring et al [38,39] have indicated that both females and males detailed encountering more sadness because of the sad movies than fear in response to the fear movies and and happiness in response to the happy movies. Besides, both females and males have displayed more positive expressions in response to the happy movies than to either of the negative movies, and more negative expressions in response to the fear movies than to the sad movies [40].…”

Section: Discussionmentioning

confidence: 95%

“…(2) Discrete wavelet change was utilized for denoising purposes, the symlets mother wavelet function of 'order 9' was utilized to give the excellent denoising results [29]. Four decomposition levels for the recorded EEG signals were chosen based on the sampling frequency of 128 Hz [10]. The Stein's unbiased risk estimation (SURE) threshold has been applied [30].…”

Section: Pre-processing Stagementioning

confidence: 99%

“…Recently, various examinations on emotional recognition have been explored utilizing electroencephalograph (EEG) [10,11]. EEG signals have been demonstrated to give educational attributes in reactions to the diverse mental states [12].…”

Section: Introductionmentioning

confidence: 99%

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Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Al-Qazzaz

Sabir

Ali

et al. 2021

IJIE

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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 wa s 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.

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Section: Discussionmentioning

confidence: 95%

Section: Pre-processing Stagementioning

confidence: 99%

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Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Al-Qazzaz

Sabir

Ali

et al. 2021

IJIE

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“…ADD, EEG işaretini detay(D) ve yaklaşık (A) katsayılarına ayrıştırır. EEG sinyalleri genellikle 0.5-4 Hz Delta(δ), 4-8 Hz Teta(θ), 8-16 Hz Alfa(α), 16-32 Hz Beta(β), 32 Hz ve üstündeki sinyaller Gama(γ) olmak üzere beş frekans bandından oluşmaktadır [20][21][22]. DD yöntemi, EEG sinyalini zamanfrekans düzlemine aktarır.…”

Section: öZnitelik çıKarımı Ve Seçimiunclassified

Feature extraction with wavelet transform for EEG Signal based anxiety classification

OMAR

Tepe

2023

NÖHÜ Müh. Bilim. Derg.

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Anksiyete, üretkenliği ve yaşam kalitesini etkilediği kadar insan yeteneklerini ve davranışlarını da etkiler. Depresyon ve intiharın ana nedeni olarak kabul edilebilir. Günümüzde klinisyenler anksiyete bozukluklarını teşhis etmek için belirli kriterler kullanılmaktadır. Anksiyete tespitinin karmaşık görevini yerine getiren, invaziv olmayan güvenilir tekniklere ihtiyaç vardır. Bu çalışma, elektroensefalografi (EEG) sinyallerini analiz ederek ikili ve dörtlü sınıfları daha az EEG kanalı ve öznitelik sınıflandırmayı amaçlamıştır. 23 kişinin 14 kanallı EEG sinyalini içeren DASPS veri tabanı kullanılmıştır. EEGLAB kullanarak 14 kanaldan 4 kanal seçilmiştir. Öznitelik çıkarımı için dalgacık dönüşümü kullanılmıştır. MATLAB Classification learner toolbox’taki 8 yöntem ile sınıflandırma yapılmıştır. En yüksek doğrulukta başarımlar ikili sınıflandırmada %67,1 doğrulukta Karar ağaçları yönteminde, dörtlü sınıflandırmada %58,5 doğrulukta destek vektör makinesi ile elde edilmiştir.

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Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis

Al-Qazzaz

Lafta

Khudhair

2023

Advances in Non-Invasive Biomedical Signal Sensing and Processing With Machine Learning

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The Role of Spectral Power Ratio in Characterizing Emotional EEG for Gender Identification

Cited by 12 publications

References 26 publications

Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Spectro-spatial Profile for Gender Identification Using Emotional-based EEG Signals

Feature extraction with wavelet transform for EEG Signal based anxiety classification

Estimations of Emotional Synchronization Indices for Brain Regions Using Electroencephalogram Signal Analysis

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