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    Emotion recognition using functional near infrared spectroscopy (fNIRS) faces challenges due to data instability. This study found significant accuracy decline and introduced a novel method to improve emotion classification stability over time.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Emotion recognition using neural signals, particularly functional near infrared spectroscopy (fNIRS), shows promise for healthcare applications.
    • Existing fNIRS-based emotion recognition systems suffer from data and classification instability due to various influencing factors, potentially leading to user dissatisfaction.

    Purpose of the Study:

    • To investigate data instability and its impact on classification accuracy in fNIRS-based emotion recognition.
    • To identify the causes of accuracy decline in emotion recognition systems using fNIRS.
    • To develop and evaluate a novel method for mitigating classification instability in fNIRS-based emotion recognition.

    Main Methods:

    • A 2-class emotion recognition test-retest experiment using fNIRS with visual stimuli and a 3-week interval.
    • Analysis of data instability using Kullback-Leibler (KL) divergence to assess changes in neural signal feature distributions.
    • Development of a new feature selection criterion combining Fisher Score for separability and KL divergence for temporal stability.

    Main Results:

    • A significant average deterioration of 22.2% in emotion classification accuracy was observed between test and retest sessions.
    • Changes in neural signal feature distributions, measured by KL divergence, were identified as a likely cause for accuracy decline.
    • Instability in spatial activation patterns and hemodynamic responses correlated with accuracy decline.

    Conclusions:

    • Classification instability is a critical issue in fNIRS-based emotion recognition, impacting system reliability.
    • The proposed feature selection method, incorporating temporal stability, significantly improved cross-session generalization accuracy by approximately 5%.