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

Author

Listed:
  • Hao Luo

    (Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
    These authors contributed equally to this work.)

  • Haobo Li

    (Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
    These authors contributed equally to this work.)

  • Wei Tao

    (Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa 999078, Macau
    Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, 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
    Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, 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
    Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Taipa 999078, Macau)

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.

Suggested Citation

  • Hao Luo & Haobo Li & Wei Tao & Yi Yang & Chio-In Ieong & Feng Wan, 2025. "A Portable and Affordable Four-Channel EEG System for Emotion Recognition with Self-Supervised Feature Learning," Mathematics, MDPI, vol. 13(10), pages 1-33, May.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1608-:d:1655601
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