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Physiology of Emotion01:20

Physiology of Emotion

The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...

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Cortical Source Analysis of High-Density EEG Recordings in Children
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REGEEG: A Regression-Based EEG Signal Processing in Emotion Recognition.

Oscar Almanza-Conejo, Juan Gabriel Avina-Cervantes, Arturo Garcia-Perez

    IEEE Journal of Biomedical and Health Informatics
    |March 10, 2025
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    Summary

    Electroencephalograms (EEG) show promise for AI emotion recognition. A novel REGEEG method with K-Nearest Neighbors achieved over 95% accuracy in classifying emotions during gameplay.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Electroencephalograms (EEG) are crucial for non-invasive study of emotion recognition.
    • Developing AI models to understand human behavior and decision-making is a key research area.

    Purpose of the Study:

    • To develop an accurate emotion recognition model using EEG signals during gameplay.
    • To test various machine learning classification kernels for optimal performance.

    Main Methods:

    • Utilized the publicly available GAMEEMO database for EEG-based emotion recognition.
    • Developed a novel signal processing method called Regression EEG (REGEEG) with an electrode pairing selector.
    • Evaluated 28 machine learning kernels, including K-Nearest Neighbors (k-NN), using statistical and polynomial feature extraction.

    Main Results:

    • Five kernels achieved over 80% classification performance.
    • The K-Nearest Neighbors (k-NN) model exceeded 95% accuracy, F1-Score, and kappa-score.
    • REGEEG demonstrated robust performance across 30-fold Cross-Validation and Leave-one Subject-out techniques.

    Conclusions:

    • The REGEEG method and EEG electrode pair channel selection are effective for emotion recognition.
    • The study highlights the potential of EEG-based AI for understanding human emotions during interactive tasks.