Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers

Overview

1. Faculty of Engineering, Institución Universitaria Pascual Bravo, Cra. 73 #73A, Medellín 520001, Colombia; (C.D.-M.); (C.Z.-H.)
2. Faculty of Business Studies, Institución Universitaria Esumer, Carrera 28 No. 19-24, Medellín 520001, Colombia
3. Faculty of Engineering, Instituto Tecnológico Metropolitano, Calle 73 No. 76A-354, Medellín 520001, Colombia

Abstract

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions.

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Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 1 funder, 53 references.

Cite

This paper

Duque-Mejia, C., Zapata-Hernandez, C., Duque-Grisales, E., Serna-Guarin, L., Lodoño-Ossa, G., & Becerra, M. A. (2026). Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers. Bioengineering (Basel, Switzerland), 13(6), 689.

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