A Portable and Affordable Four-Channel EEG System for Emotion Recognition with Self-Supervised Feature Learning

Hao Luo, Haobo Li, Wei Tao, Yi Yang, Chio-In Ieong () and Feng Wan ()
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Hao Luo: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
Haobo Li: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
Wei Tao: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
Yi Yang: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
Chio-In Ieong: Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China
Feng Wan: Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau

Mathematics, 2025, vol. 13, issue 10, 1-33

Abstract: Emotions play a pivotal role in shaping human decision-making, behavior, and physiological well-being. Electroencephalography (EEG)-based emotion recognition offers promising avenues for real-time self-monitoring and affective computing applications. However, existing commercial solutions are often hindered by high costs, complicated deployment processes, and limited reliability in practical settings. To address these challenges, we propose a low-cost, self-adaptive wearable EEG system for emotion recognition through a hardware–algorithm co-design approach. The proposed system is a four-channel wireless EEG acquisition device supporting both dry and wet electrodes, with a component cost below USD 35. It features over 7 h of continuous operation, plug-and-play functionality, and modular expandability. At the algorithmic level, we introduce a self-supervised feature extraction framework that combines contrastive learning and masked prediction tasks, enabling robust emotional feature learning from a limited number of EEG channels with constrained signal quality. Our approach attains the highest performance of 60.2% accuracy and 59.4% Macro-F1 score on our proposed platform. Compared to conventional feature-based approaches, it demonstrates a maximum accuracy improvement of up to 20.4% using a multilayer perceptron classifier in our experiment.

Keywords: electroencephalogram (EEG); affective computing; few-channel EEG; wearable EEG; self-supervised learning; feature extraction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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