Open Access

ARTICLE

Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

Abdullah A Asiri¹, Akhtar Badshah², Fazal Muhammad^3,*, Hassan A Alshamrani¹, Khalil Ullah⁴, Khalaf A Alshamrani¹, Samar Alqhtani⁵, Muhammad Irfan⁶, Hanan Talal Halawani⁷, Khlood M Mehdar⁸

1 Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
2 Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan
3 Department of Electrical Engineering, University of Engineering and Technology, Mardan, 23200, Pakistan
4 Department of Software Engineering, University of Malakand, Dir Lower, Pakistan
5 Department of Computer Science and Information Systems, College of Engineering, Najran University Saudi Arabia, Najran, 61441, Saudi Arabia
6 Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran, 61441, Saudi Arabia
7 Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
8 Anatomy Department, Medicine College, Najran University, Najran, Saudi Arabia

* Corresponding Author: Fazal Muhammad. Email:

Computers, Materials & Continua 2022, 73(3), 5075-5089. https://doi.org/10.32604/cmc.2022.031156

Received 11 April 2022; Accepted 29 May 2022; Issue published 28 July 2022

Abstract

Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the mentioned emotions. Using a bandpass filter with a bandwidth of 1–100 Hz, recorded raw EEG signals are preprocessed. The preprocessed signals then further analyzed and twelve selected features in different domains are extracted. The Random forest (RF) and multilayer perceptron (MLP) algorithms are then used for the classification of the emotions through extracted features. The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80% and 88% using MLP and RF classifiers respectively on hybrid features for experimental signals of different subjects. The proposed model outperforms in terms of cost and accuracy.

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