EEG-Based Affective Computing: A Review of Signal Processing Techniques

Linlin Su

doi:10.54254/2755-2721/2025.LD26298

Applied and Computational EngineeringOpen access

EEG-Based Affective Computing: A Review of Signal Processing Techniques

Research Article

Open Access

EEG-Based Affective Computing: A Review of Signal Processing Techniques

Linlin Su ^1*

¹ Huamei-Bond International School, Guangzhou, China, 510520

^*Corresponding author: sulin20070910@163.com

Published on 26 August 2025

ACE Vol.179

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-184-6

ISBN (Online): 978-1-80590-129-7

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Abstract

As intelligent human-computer interaction (HCI) evolves, the ability of systems to accurately perceive and respond to human emotions has become increasingly crucial. Emotional perception allows machines to adapt and react empathetically, making interactions more natural and engaging. This paper reviews current EEG-based emotion recognition techniques, focusing on key steps such as preprocessing, feature extraction, and machine learning models. Specifically, we explore various models like Support Vector Machine (SVM), Long Short-Term Memory (LSTM) networks, and Deep Belief Networks (DBN), all of which have demonstrated promising results in classifying emotional states from EEG signals. In addition, we compare some of the most recent approaches in the field, including MCD_DA—a method developed at Hebei University of Technology. This technique addresses the challenge of cross-subject adaptation, where recognising emotions in new individuals, not seen during training, is crucial for real-world applications. Many emotion recognition systems struggle with generalizing to new subjects due to individual differences in brainwave patterns. MCD_DA attempts to solve this problem, making the technology more robust and scalable.

Keywords:

Emotion Recognition, EEG Signals, Machine Learning Models

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References

[1]. Wyler, A. R., Ojemann, G. A., & Ward Jr, A. A. (1982). Neurons in human epileptic cortex: correlation between unit and EEG activity. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 11(3), 301-308.

[2]. Shi, L. C., Jiao, Y. Y., & Lu, B. L. (2013, July). Differential entropy feature for EEG-based vigilance estimation. In 2013, the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6627-6630). IEEE.

[3]. Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from EEG. IEEE Transactions on Affective Computing, 5(3), 327-339.

[4]. Boutsidis, C., Mahoney, M. W., & Drineas, P. (2008, August). Unsupervised feature selection for principal components analysis. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 61-69).

[5]. Wang, X. W., Nie, D., & Lu, B. L. (2011, November). EEG-based emotion recognition using frequency domain features and support vector machines. In International Conference on Neural Information Processing (pp. 734-743). Berlin, Heidelberg: Springer Berlin Heidelberg.

[6]. Sohn, I. (2021). Deep belief network-based intrusion detection techniques: A survey. Expert Systems with Applications, 167, 114170.

[7]. Dose, H., Møller, J. S., Iversen, H. K., & Puthusserypady, S. (2018). An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Systems with Applications, 114, 532-542.

[8]. Saito, K., Watanabe, K., Ushiku, Y., & Harada, T. (2018). Maximum classifier discrepancy for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3723-3732).

References

[3]. Jenke, R., Peer, A., & Buss, M. (2014). Feature extraction and selection for emotion recognition from EEG. IEEE Transactions on Affective Computing, 5(3), 327-339.

[6]. Sohn, I. (2021). Deep belief network-based intrusion detection techniques: A survey. Expert Systems with Applications, 167, 114170.

Cite this article

Su,L. (2025). EEG-Based Affective Computing: A Review of Signal Processing Techniques. Applied and Computational Engineering,179,50-55.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-184-6(Print) / 978-1-80590-129-7(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.179

ISSN: 2755-2721(Print) / 2755-273X(Online)

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