Human Emotion Detection Using Eeg Signals: Insights Into Feature Selection

Awais, Muhammad and Yesypenko, Yeva (2025) Human Emotion Detection Using Eeg Signals: Insights Into Feature Selection. In: 2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE). The Institute of Electrical and Electronics Engineers (IEEE). ISBN 979-8-3315-5900-7

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Abstract

Human emotions are complex phenomena and are closely linked with the physical and mental health of any individual. Therefore, accurate detection of human emotions is strongly relevant to quality of life and well-being. Brain signals such as electroencephalograms (EEG) in this regard are among the leading imaging modalities that can classify human emotions. However, EEG signals are complex, and EEGbased emotion recognition systems process hundreds of features, which makes EEG interpretation difficult and computationally complex. This is because not all features are equally important. Therefore, this study examines a pool of EEG features and investigates which features are essential for emotion detection. We developed a hybrid feature selection method that combines statistical, information-based, and tree-based approaches with brain research knowledge. Using the SEED dataset (310 features, 20 subjects, 41,160 samples), the method identified 29 important features. This is a 90.6 % reduction while keeping 88−92% of the original performance. Results show theta band waves (48 %) are the most useful features. Temporal brain regions (35 %) and frontal regions (28 %) are also important. Random Forest classification achieves 74.9% accuracy with the reduced features. It shows good performance across all emotion types (F1-scores: 0.805 negative, 0.613 neutral, 0.817 positive). The findings show that the hybrid approach performs well in balancing efficiency and accuracy and can potentially be extended to develop portable, real-time emotion recognition device in real-life conditions.

Item Type:	Book Section
Uncontrolled Keywords:	eeg,emotion recognition,feature selection,machine learning,random forest,svm,brain-computer interfaces,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Computing
Depositing User:	LivePure Connector
Date Deposited:	11 Feb 2026 11:30
Last Modified:	16 Feb 2026 01:02
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101910
DOI:	10.1109/BIBE66822.2025.00103

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