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HEDN: A Hard-Easy Dual Network with Source Reliability Assessment for Cross-Subject EEG Emotion Recognition.

Qiang Wang, Liying Yang, Jiayun Song

    IEEE Journal of Biomedical and Health Informatics
    |July 1, 2026
    PubMed
    Summary

    This study introduces a novel framework for electroencephalography (EEG) emotion recognition, improving accuracy in brain-computer interfaces by intelligently adapting data from different users. The method enhances reliability and reduces computational load.

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    Area of Science:

    • Neuroscience
    • Computer Science
    • Biomedical Engineering

    Background:

    • Cross-subject electroencephalography (EEG) emotion recognition is crucial for brain-computer interfaces (BCI) and wearable health.
    • Inter-subject variability in EEG data presents a significant challenge for reliable emotion recognition.
    • Existing Multi-Source Domain Adaptation (MSDA) methods often struggle with varying source data quality and computational demands.

    Purpose of the Study:

    • To develop a lightweight, reliability-aware MSDA framework for cross-subject EEG emotion recognition.
    • To address the limitations of existing MSDA approaches, including negative transfer and high computational overhead.
    • To improve the accuracy and efficiency of emotion recognition in BCI and wearable health applications.

    Main Methods:

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    • Proposed the Hard-Easy Dual Network (HEDN), a novel MSDA framework.
    • Introduced a Source Reliability Assessment (SRA) mechanism to dynamically evaluate source domain quality.
    • Implemented specialized Easy and Hard Networks with a cross-network consistency loss for robust adaptation.

    Main Results:

    • HEDN achieved highly competitive performance compared to state-of-the-art methods on SEED, SEED IV, and DEAP datasets.
    • Demonstrated significant reduction in adaptation complexity and computational overhead.
    • Showcased the effectiveness of the reliability-aware approach in handling inter-subject variability.

    Conclusions:

    • The proposed HEDN framework offers an effective and efficient solution for cross-subject EEG emotion recognition.
    • Reliability assessment and dual-network architecture are key to overcoming challenges in MSDA for BCI applications.
    • HEDN provides a promising direction for advancing real-time cognitive and affective state monitoring.