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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Alwan Tuib, Tabarek
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Computer Science & IT Engineering

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Convolutional neural network with binary moth flame optimization for emotion detection in electroencephalogram Alwan Tuib, Tabarek; Saoudi, Baydaa Hadi; Hussein, Yaqdhan Mahmood; Mandeel, Thulfiqar H.; Al-Dhief, Fahad Taha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1172-1178

Abstract

Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states. 
Co-Authors Al-Dhief, Fahad Taha Hussein, Yaqdhan Mahmood Mandeel, Thulfiqar H. Saoudi, Baydaa Hadi