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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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Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension

Luis R Mercado-Diaz1, Youngsun Kong1, Josef Kundrát1,2

  • 1Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

Applied Sciences (Basel, Switzerland)
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

We developed a novel electrodermal activity (EDA) graph signal processing method for emotion recognition. This approach significantly improves the accuracy of detecting arousal and valence, outperforming existing methods in various real-world scenarios.

Keywords:
affective dimensionsarousalelectrodermal activityemotion recognitionemotional statesgraph signal processingmachine learningvalence

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Area of Science:

  • Physiological computing
  • Affective computing
  • Machine learning for healthcare

Background:

  • Emotion recognition from physiological signals is crucial for healthcare and human-computer interaction.
  • Electrodermal activity (EDA) is a sensitive indicator of emotional states.
  • Existing methods for analyzing EDA signals have limitations in capturing nuanced affective dimensions.

Purpose of the Study:

  • To develop and evaluate a novel graph signal processing pipeline for electrodermal activity (EDA) signals.
  • To enhance the sensitivity and accuracy of emotion recognition, specifically for arousal and valence.
  • To compare the performance of graph-based EDA features against traditional EDA features and other physiological signals.

Main Methods:

  • Developed an electrodermal activity (EDA)-graph signal processing pipeline to extract sensitive features.
  • Utilized the Continuously Annotated Signals of Emotion dataset for initial model training and validation.
  • Compared graph-based EDA features (EDA-graph) with time-domain, frequency-domain EDA features, and features from heart rate variability, pulse transit time, electromyography, skin temperature, and respiration.
  • Employed machine learning regression models for continuous affective dimension recognition.
  • Validated the approach on diverse datasets from laboratory and ambulatory settings.

Main Results:

  • The EDA-graph features demonstrated superior performance in continuous arousal and valence recognition, achieving RMSE values of 0.801 and 0.714, respectively.
  • Models achieved high accuracy across various datasets: 98.2% for positive, negative, and mixed emotions; 92.75% for discriminating specific emotions; and 86.54% for stress detection.
  • The graph-based approach showed robust generalization capabilities across different emotional contexts and settings.

Conclusions:

  • A graph-based analysis of electrodermal activity (EDA) offers a highly effective method for emotion recognition.
  • This approach significantly advances the state-of-the-art in continuous affective dimension detection.
  • The developed method holds considerable potential for real-world emotion recognition systems in healthcare and human-computer interaction.