Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals
Attia, A., Amin, K., ibrahim, M. (2023). Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals. EKB Journal Management System, 10(2), 54-65. doi: 10.21608/ijci.2023.199502.1099
Aseel Mahmoud Attia; Khalid M. Amin; mina ibrahim. "Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals". EKB Journal Management System, 10, 2, 2023, 54-65. doi: 10.21608/ijci.2023.199502.1099
Attia, A., Amin, K., ibrahim, M. (2023). 'Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals', EKB Journal Management System, 10(2), pp. 54-65. doi: 10.21608/ijci.2023.199502.1099
Attia, A., Amin, K., ibrahim, M. Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals. EKB Journal Management System, 2023; 10(2): 54-65. doi: 10.21608/ijci.2023.199502.1099

Machine Learning Algorithms for Enhancing Emotion Recognition from EEG Signals

IJCI. International Journal of Computers and Information
Article 6, Volume 10, Issue 2, September 2023, Page 54-65  XML PDF (618.36 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.199502.1099
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Authors
Aseel Mahmoud Attia email 1; Khalid M. Aminorcid 2; mina ibrahim3
1Information Technology, Faculty of Computers and Information, Menofia University
2Information technology dept., Faculty of computers and information, Menofia university
3information technology department, faculty of computers and information
Abstract
Emotion recognition through electroencephalography (EEG) signals is an important aspect of human-computer interaction that poses a significant research challenge. Most of the current approaches utilize up to 18 channels from 32 available channels for extracting emotions features. Moreover, they only use valence and arousal model to classify emotions. Therefore, the current approaches are unable to detect emotions accurately. In this paper, a framework that utilizes a three-dimensional model incorporating arousal, valence, and dominance for identifying emotions is proposed. Our framework can define any number of emotions, even in the absence of discrete emotions labels. The electroencephalography signals from DEAP database are utilized for emotion detection. The effectiveness of three classification techniques is examined, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). The classification accuracies for valence, arousal, and dominance are 90.19%, 91.91%, and 89.86%, respectively. Our results demonstrate that EEG data's time-domain statistical and power features can effectively classify different emotional states. Furthermore, our framework enables accurate identification of identical emotions that cannot be distinguished by a two-dimensional model.
Keywords
Emotion recognition; Machine learning; Classification; Valence-Arousal-Dominance model
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