METHODS OF CLASSIFICATION OF ELECTROENCEPHALOGRAM DATA FOR IDENTIFICATION OF HUMAN EMOTIONAL STATE
DOI:
https://doi.org/10.30888/2709-2267.2025-31-00-016Keywords:
electroencephalogram, emotion recognition, cluster analysis, statistical analysis, barycenter signatureAbstract
The developed methods for classifying electroencephalogram (EEG) signals enable the identification of brain responses to emotional stimuli, which serves as the foundation for constructing neurointerface systems. The result of the classification process isReferences
Bota, P., Wang, C., & Leu, M. C. (2019). Electroencephalogram-Based Emotion Recognition: A Comparative Analysis of Different Feature Extraction Methods. Cognitive Neurodynamics, 13(5), 421–434.
Egger, M., Ley, M., & Hanke, S. (2019). Emotion Recognition from EEG Signals with Different Feature Extraction Techniques. Procedia Computer Science, 159, 31–38.
Lin, Y., & Li, Y. (2023). A Review on EEG-Based Emotion Recognition: Methods and Applications. Frontiers in Neuroscience, 17, 123456.
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Published
2025-05-30
How to Cite
Holoborodko, V., & Lyfar, V. (2025). METHODS OF CLASSIFICATION OF ELECTROENCEPHALOGRAM DATA FOR IDENTIFICATION OF HUMAN EMOTIONAL STATE. Sworld-Us Conference Proceedings, 1(usc31-00), 27–30. https://doi.org/10.30888/2709-2267.2025-31-00-016
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