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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Shaheed Al-Azzawi, Dheyaa
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Computer Science & IT Engineering

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Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines Hamid Zghair, Ghufran; Shaheed Al-Azzawi, Dheyaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3671-3685

Abstract

Recently, the use of artificial intelligence techniques has become widespread, having been adopted in brain-computer interfaces (BCIs) with electroencephalograms (EEGs). BCIs allow direct communication between a person's brain and a computer, and have various uses ranging from assistive technology to neuroscientific study. This paper provides an introductory overview of BCIs and EEG. We adopted the use of machine learning (ML) algorithms, including K-nearest neighbors (KNN), logistic regression, decision trees, random forests, and support vector machine (SVM). Additionally, we proposed a hybrid model of deep learning (DL) and ML by combining convolutional neural networks (CNNs) and SVMs. Our achieved 98% accuracy. The goal is to classify EEG signals into three emotional states: happy, normal, and sad. The study aims to achieve a comprehensive understanding of the effectiveness of these algorithms in accurately classifying emotional states based on EEG data. By comparing the performance of traditional ML methods and the proposed hybrid model, we seek to identify the most robust and accurate approach to sentiment classification.
Co-Authors Hamid Zghair, Ghufran