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    Eye movement data alone effectively recognizes emotions within individuals, achieving 99.92% accuracy. However, generalizing emotion recognition across different people remains challenging, highlighting the need for subject-specific models.

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    Area of Science:

    • Affective computing
    • Human-computer interaction
    • Biometric signal processing

    Background:

    • Emotion recognition is vital for mental health and HCI.
    • Eye movement data offers a non-invasive method for emotion detection.
    • Existing methods often rely on complex multimodal data.

    Purpose of the Study:

    • To investigate the efficacy of eye movement features for emotion classification.
    • To evaluate the Deep Generalized Canonical Correlation Analysis with Attention Mechanism (DGCCA-AM) framework for this task.
    • To assess both intra-subject and inter-subject emotion recognition performance.

    Main Methods:

    • Utilized the SEED-IV dataset with 31 eye movement features from 15 subjects.
    • Employed the DGCCA-AM framework for emotion classification.
    • Conducted subject-dependent and subject-independent evaluations using a leave-one-subject-out strategy.

    Main Results:

    • Achieved 99.92% accuracy in intra-subject emotion recognition (happy, sad, fear, neutral).
    • Obtained 63.14% average accuracy in inter-subject recognition, indicating generalization challenges.
    • Identified pupil features as the most significant contributors, with a 6% accuracy drop when excluded.

    Conclusions:

    • Eye movement data alone is highly effective for within-subject emotion recognition.
    • Cross-subject generalization of emotion recognition from eye movements is challenging.
    • Subject-specific modeling is crucial, and further research should focus on improving cross-subject adaptability.