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. 2021 Sep 21:2021:8537000.
doi: 10.1155/2021/8537000. eCollection 2021.

Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs

Noor Kamal Al-Qazzaz  1   2 , Mohannad K Sabir  1 , Sawal Hamid Bin Mohd Ali  2 , Siti Anom Ahmad  3   4 , Karl Grammer  5
Affiliations

Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs

Noor Kamal Al-Qazzaz et al. J Healthc Eng. .

Abstract

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 (Hur) and amplitude-aware permutation entropy (AAPE) features were extracted from the EEG dataset. k-nearest neighbors (kNN) 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 Hur as a complexity feature and AAPE 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 CompEn hybrid feature fusion method towards developing the novel WT_CompEn 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 Hur and AAPE features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed WT_CompEn framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel WT_CompEn may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel WT_CompEn 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.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
The block diagram of the proposed study.
Figure 2
Figure 2
The experimental protocol of emotion [9].
Figure 3
Figure 3
Setup of the experimental room with presentation TV and the recorders.
Figure 4
Figure 4
The denoising results after preprocessing stage for channel F7.
Figure 5
Figure 5
Boxplots for the Hur feature extraction method for gender distribution from emotional-based EEG signals. The dark black line represents the median values.
Figure 6
Figure 6
Comparative plot of the four tested emotional states for females and males using Hurst exponent complexity feature.
Figure 7
Figure 7
Boxplots for the AAPE feature extraction method for gender distribution from emotional-based EEG signals. The dark black line represents the median values.
Figure 8
Figure 8
Comparative plot of the four tested emotional states for females and males using amplitude-aware permutation entropy feature.
Figure 9
Figure 9
ROC curve and the AUC values of gender classification from emotional-based EEGs using Hurst exponent features and kNN classifier.
Figure 10
Figure 10
ROC curve and the AUC values of gender classification from emotional-based EEGs using Hurst exponent features and SVM classifier.
Figure 11
Figure 11
ROC curve and the AUC values of gender classification from emotional-based EEGs using amplitude-aware permutation entropy and kNN classifier.
Figure 12
Figure 12
ROC curve and the AUC values of gender classification from emotional-based EEGs using amplitude-aware permutation entropy and SVM classifier.
Figure 13
Figure 13
ROC curve and the AUC values of gender classification from emotional-based EEGs using proposed CompEn hybrid features and kNN classifier.
Figure 14
Figure 14
ROC curve and the AUC values of gender classification from emotional-based EEGs using proposed CompEn hybrid features using SVM classifier.
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