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

Noor Kamal Al-Qazzaz,Karl Grammer,Mohannad K Sabir,Sawal Hamid Bin Mohd Ali,Siti Anom Ahmad,Cosimo Ieracitano

doi:10.1155/2021/8537000

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.

Highlights

Perceiving gender based on human emotions has gained lots of research interest to investigate personal characteristics in neuroscience and psychology [1]
KNN and support vector machine (SVM) classification techniques were used for automatic gender identification of emotional-based EEG datasets. e performances of these classifiers were examined on Hurst exponent (Hur) and aware permutation entropy (AAPE) individually and on the complexity and irregularity features (CompEn) feature set
Results of Classification Stage. is study has dealt with emotional-based EEG signals for gender identification problems. e key design decisions for k-nearest neighbors (kNN) and SVM used in the classification are the training process, as they depend on the size of the training set and the test set

Summary

Introduction

Perceiving gender based on human emotions has gained lots of research interest to investigate personal characteristics in neuroscience and psychology [1]. Us far, few researchers have investigated gender variations primarily based on emotional changes [3], and most of them report substantial differences [2]. To reveal personal characteristics that would be valuable in recognizing individual gender accurately in daily life, visual and auditory stimuli are considered as two common ways for human beings to elicit different emotional states [2]. Audiovisual elicitations utilizing short film video clips are usually used to elicit various conditions of emotion better compared to the other modalities [4, 6,7,8,9]. In this work, emotions were precipitated with the aid of the use of short audiovisual video clips

Objectives

Methods

Results

Conclusion