A Decision Science Approach Using Hybrid EEG Feature Extraction and GAN-Based Emotion Classification

Oshamah Ibrahim Khalaf; Ashokkumar. S.R; Dhanasekaran S; Ghaida Muttashar Abdulsahib; Premkumar. M

doi:10.47654/v27y2023i1p172-191

Authors

Oshamah Ibrahim Khalaf Department of Solar, Al-Nahrain Research Center for renewable Energy, Al-Nahrain University, Jadriya, Baghdad,Iraq Author
Ashokkumar. S.R Sri Eshwar College of Engineering, Coimbatore, India-641202 Author
S.Dhanasekaran Sri Eshwar College of Engineering, Coimbatore, India-641202 Corresponding Author
Ghaida Muttashar Abdulsahib Department of Computer Engineering, University of Technology, Baghdad, Iraq Author
Premkumar. M SSM Institute of Engineering and Technology, Dindigul, India Author

DOI:

https://doi.org/10.47654/v27y2023i1p172-191

Keywords:

EEG, Emotions, DEAP Data, GAN, Independent Component Analysis

Abstract

Purpose: Emotions play an essential role in human life and they profoundly influence behavior, decision-making, and well-being. This approach aims to classify human emotions by using Generative Adversarial Networks (GAN) and hybrid Electroencephalography (EEG) features with the DEAP dataset.

Design/methodology/approach: The proposed system addresses the limitations of traditional classification techniques by generating synthetic hybrid features that capture additional information about emotional states. Informed by decision science principles, the system recognizes that emotions heavily influence human decision-making processes.

Findings: The process consists of data collection, pre-processing, feature extraction, GAN training, hybrid feature generation, and classification. The DEAP dataset is pre-processed by using Independent Component Analysis (ICA) and Wavelet Transform to remove artifacts. A GAN model is trained to generate synthetic features that mimic the distribution of real EEG signals. The hybrid features are generated by combining the real EEG features and synthetic features.

Originality/value: The performance of the classification system is evaluated using accuracy at 97.4%, precision at 97.22%, recall at 96.8%, and F1 score at 97.08%. By leveraging EEG signals, the proposed system shows promise in enhancing the accuracy of emotion classification, opening up exciting avenues for future research in this domain.

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