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    This study introduces a novel point process state-space model to continuously track psychological arousal using only skin conductance signals. The method accurately estimates arousal levels during stress and fear experiments.

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    Area of Science:

    • Neuroscience
    • Psychophysiology
    • Signal Processing

    Background:

    • Brain structures process emotions, influencing physiological signals like skin conductance.
    • Skin conductance signals contain information in both continuous waves and impulsive events.
    • Tracking psychological arousal is crucial for understanding emotional states.

    Purpose of the Study:

    • To develop a method for continuously tracking psychological arousal from skin conductance.
    • To utilize a point process state-space model with Bayesian filtering for arousal estimation.

    Main Methods:

    • A point process state-space model with Bayesian filtering was employed.
    • Arousal was related to phasic and tonic skin conductance signal components.
    • Model parameters were optimized using the expectation-maximization algorithm.
    • The model was validated on synthetic and experimental datasets, including induced stress and fear conditioning.

    Main Results:

    • The model demonstrated high arousal detection during cognitive stress and low arousal during relaxation.
    • Results in the fear conditioning experiment were consistent with prior skin conductance studies.
    • The state-space approach successfully tracked arousal levels without external classification labels.

    Conclusions:

    • The developed state-space approach provides a continuous arousal tracking method using only skin conductance.
    • This method shows promise for wearable monitoring and studying neuropsychiatric conditions like PTSD.