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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

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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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Related Experiment Video

Updated: Sep 23, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

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Published on: November 1, 2019

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OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition.

Yong Peng, Fengzhe Jin, Wanzeng Kong

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 16, 2022
    PubMed
    Summary

    This study introduces an Optimal Graph coupled Semi-Supervised Learning (OGSSL) model for enhanced emotion recognition using electroencephalogram (EEG) signals. The OGSSL model unifies graph learning and recognition for improved accuracy and identifies key brain regions and frequencies linked to emotions.

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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Signal Processing

    Background:

    • Electroencephalogram (EEG) signals are valuable for emotion recognition due to their inherent difficulty in disguise.
    • Semi-supervised learning shows promise by incorporating unlabeled EEG data, but traditional graph-based methods and label propagation have limitations in collaboration.
    • Existing methods face challenges in effectively integrating graph structure learning with the emotion recognition task.

    Purpose of the Study:

    • To propose an Optimal Graph coupled Semi-Supervised Learning (OGSSL) model for EEG-based emotion recognition.
    • To unify adaptive graph learning and emotion recognition into a single objective for improved performance.
    • To automatically identify discriminative EEG frequency bands and brain regions associated with emotional states.

    Main Methods:

    • Developed an Optimal Graph coupled Semi-Supervised Learning (OGSSL) model.
    • Integrated adaptive graph learning and emotion recognition within a unified objective function.
    • Improved the label indicator matrix for unlabeled samples to directly infer emotional states.
    • Utilized a projection matrix within OGSSL for automatic recognition of key EEG frequency bands and brain regions.

    Main Results:

    • Achieved excellent average accuracies of 76.51%, 77.08%, and 81.29% in cross-session emotion recognition tasks on the SEED-IV dataset.
    • Demonstrated the model's competence in discriminative EEG feature selection for emotion recognition.
    • Identified the Gamma frequency band and specific brain regions (temporal, prefrontal, parietal lobes) as highly correlated with emotional occurrences.

    Conclusions:

    • The proposed OGSSL model offers a significant advancement in EEG-based emotion recognition by effectively integrating graph learning and semi-supervised learning.
    • OGSSL facilitates automatic feature selection, highlighting the importance of specific EEG frequency bands and brain regions in emotional processing.
    • The findings provide valuable insights into the neural correlates of emotion, paving the way for more sophisticated brain-computer interfaces.