Abstract
Background: Human emotions are subjective psychophysiological processes that play an important role in the daily interactions of human life. Emotions often do not manifest themselves in isolation; people can experience a mixture of them and may not express them in a visible or perceptible way; Methods: This study seeks to uncover EEG patterns linked to emotions, as well as to examine brain activity across emotional states and optimise machine learning techniques for accurate emotion classification. For these purposes, the DEAP dataset was used to comprehensively analyse electroencephalogram (EEG) data and understand how emotional patterns can be observed. Machine learning algorithms, such as SVM, MLP, and RF, were implemented to predict valence and arousal classifications for different combinations of frequency bands and brain regions; Results: The analysis reaffirms the value of EEG as a tool for objective emotion detection, demonstrating its potential in both clinical and technological contexts. By highlighting the benefits of using fewer electrodes, this study emphasises the feasibility of creating more accessible and user-friendly emotion recognition systems; Conclusions: Further improvements in feature extraction and model generalisation are necessary for clinical applications. This study highlights not only the potential of emotion classification to develop biomedical applications, but also to enhance human–machine interaction systems.
Published Version
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