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Labeling Emotion01:20

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...

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Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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EEG-Based Pain Classification via Sample Selection to Mitigate Subjective Label Bias.

Euijin Jung, Sung Chan Jun, Jinung An

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 12, 2026
    PubMed
    Summary

    This study introduces a new method to improve electroencephalography (EEG) pain assessment by selecting reliable data samples. This enhances objective pain level classification for non-communicative patients.

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    Published on: October 24, 2012

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Pain Research

    Background:

    • Accurate pain quantification is crucial for personalized pain management.
    • Electroencephalography (EEG)-based pain estimation is promising, especially for non-communicative patients.
    • Existing EEG pain models suffer from subjectivity due to self-reported pain labels, limiting reliability.

    Purpose of the Study:

    • To develop a novel method for EEG-based pain level classification using reliable sample selection during training.
    • To enhance the robustness and reliability of EEG pain prediction models by addressing label subjectivity.
    • To identify objective EEG biomarkers for pain intensity in non-communicative individuals.

    Main Methods:

    • Proposed a reliable sample selection technique that quantifies sample informativeness and label reliability.
    • Excluded unreliable or uninformative samples to improve model robustness.
    • Evaluated the method using EEG data from 41 participants under various thermal stimuli, with pain labels from the Numerical Rating Scale (NRS), employing 5-fold cross-validation.

    Main Results:

    • The proposed method achieved statistically significant improvements in multi-class EEG pain classification (3, 6, and 10 classes) compared to baseline models.
    • Demonstrated generalization to novel thermal stimulation types, indicating potential for objective pain assessment.
    • Identified delta-band activity at frontotemporal electrodes (F7, F8) as a key EEG feature strongly associated with perceived pain intensity.

    Conclusions:

    • Reliable sample selection significantly enhances the accuracy and robustness of EEG-based pain classification.
    • The developed method shows promise for objective pain assessment in challenging patient populations, such as those unable to communicate.
    • Specific EEG features, particularly delta-band activity, can serve as objective indicators of pain intensity.