Abstract This paper presents a person-independent Emotion and Gender Prediction (EGP) system using Electroencephalography (EEG) brain signals. First, Short Time Fourier Transform (STFT) technique is implemented to get the time-frequency information for the selected electrode (Fz Electrode). Then, it is splitted into twenty sequential batches according to the electrode recorded time in seconds, and the maximum EEG activation voltage is located for every frequency level within each batch to create a 2D time-frequency extraction feature. Next, sparse auto encoder is applied to convert the distribution of the extracted feature into more compact and distinguished one instead. For system evaluation, Human-Computer Interaction) database (MAHNOB-HCI) public dataset with five-fold-cross validation classifier are used and implemented. In experiments, the proposed extracted feature improves the results of both emotion and gender prediction. For emotion prediction, the highest average accuracy is 97.07\%, 93.27% and 91.72\% for three, four and six emotions with Convolutional Neural Network (CNN) classifier, respectively. While, for gender prediction, experiments are tested related to neutral, amusement, happy, sad, and the mix of all these emotions, the highest average accuracy is obtained with CNN classifier in all emotion states (>95%) including the state of mixing all emotions together. As well as, the ability to distinguish between genders in case of mixing different emotions together is practically approved.

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[42] Zhang Z., “Introduction to Machine Learning: K- Nearest Neighbors,” Annals of Translational Medicine, vo. 4, no. 11, 2016. 638 The International Arab Journal of Information Technology, Vol. 19, No. 4, July 2022 Haitham Issa received his Ph.D. in Communications and Information systems from Zhejiang University, Hangzhou, China, in 2002. He is currently teaching in the Department of Electrical Engineering in Zarqa University, Zarqa, Jordan. His main research interests include machine learning, signal processing, electronics and communications. Qinmu Peng received the Ph.D. in Computer Science, Hong Kong Baptist University, Hong Kong, in 2015. He is currently teaching in the Department of Electrical Information and Communications, Huazhong University of Science and Technology, Wuhan, China. His main research interests include machine learning, medical image processing, and pattern recognition. Sali Issa received her Ph.D. in Electrical Information and Communication Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2020. She is currently teaching in the Department of Electrical Information of Science and Technology in Hubei University of Education, Wuhan, China. Her main research interests include artificial intelligence, signal processing, and brain computer interface. Xinge You received the Ph.D. degree in computer Science from Hong Kong Baptist University, Hong Kong, in 2004. He is currently a professor in the School of Electrical Information and Communications, Huazhong University of Science and Technology, Wuhan, China. Ruijiao Peng graduated from Hubei University of Traditional Chinese Medicine in 1995. She is currently a doctor at Baiguo Hospital, Macheng City, China. Jing Wang received Ph.D. degree in Radiology from Huazhong University of Science and Technology, Wuhan, China, in 2010. She is currently an associate professor in Radiology Department in Union hospital, Wuhan, China.