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Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques.

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Summary

This study uses electroencephalography (EEG) brain waves to classify complex human emotions like pleasure and grief, particularly in individuals with alexithymia. Machine learning models successfully identified these emotions from physiological data in adolescents.

Keywords:
affective computingbrainwave signalselectroencephalogram (EEG)emotion classificationfeature selection distant discriminant (FSDD)multimodal stimulationsupport vector machineuniform manifold approximation and projection (UMAP)

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Area of Science:

  • Affective computing
  • Neuroscience
  • Psychology

Background:

  • Automated emotion identification often relies on brain signals, with Electroencephalography (EEG) being a key modality.
  • Current EEG-based affective computing primarily uses facial, speech, or text recognition, often neglecting complex emotional states.
  • Alexithymia, a psychological disorder affecting emotion processing, is prevalent in populations experiencing political instability and conflict.

Purpose of the Study:

  • To develop and implement a methodology for identifying and codifying discrete complex emotions (pleasure, grief) in adolescents.
  • To investigate the potential of EEG signals for emotion classification in individuals with alexithymia.
  • To explore emotion recognition in a challenging demographic and geopolitical context.

Main Methods:

  • Collected physiological data, including EEG, from adolescents in a multimodal virtual environment simulating emotional experiences.
  • Applied time-frequency analysis and amplitude time series correlates, including frontal alpha asymmetry, using complex Morlet wavelet.
  • Utilized Uniform Manifold Approximation and Projection (UMAP) for data visualization and employed 5-fold cross-validation with 1-second window subjective classification.

Main Results:

  • Demonstrated the feasibility of using EEG signals to differentiate between complex emotional states.
  • Identified specific brain wave patterns associated with pleasure and grief.
  • UMAP visualization provided clear distinctions between emotional states.

Conclusions:

  • EEG-based analysis is a viable method for classifying complex emotions, even in individuals with alexithymia.
  • The developed methodology shows promise for applications in mental health monitoring and affective computing.
  • Further research can refine emotion classification accuracy and explore broader applications across diverse populations.