A Novel Method for Gender and Age Detection Based on EEG Brain Signals

A Novel Method for Gender and Age Detection

Based on EEG Brain Signals

Haitham Issa1, Sali Issa2, and Wahab Shah3

1Department of Electrical Engineering, Zarqa University, Jordan

2Department of Computer Engineering, Hubei University of Education, China

3Department of Electrical Engineering, Namal University, Pakistan

Abstract: This paper presents a new gender and age classification system based on Electroencephalography (EEG) brain signals. First, Continuous Wavelet Transform (CWT) technique is used to get the time-frequency information of only one EEG electrode for eight distinct emotional states instead of the ordinary neutral or relax states. Then, sequential steps are implemented to extract the improved grayscale image feature. For system evaluation, a three-fold-cross validation strategy is applied to construct four different classifiers. The experimental test shows that the proposed extracted feature with Convolutional Neural Network (CNN) classifier improves the performance of both gender and age classification, and achieves an average accuracy of 96.3% and 89% for gender and age classification, respectively. Moreover, the ability to predict human gender and age during the mood of different emotional states is practically approved.

Keywords: EEG, Gender, Age, CWT, CNN.

Received September 27, 2020; accepted February 9, 2021

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