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EEG based emotion recognition using minimum spanning tree.

Sajjad Farashi1,2, Reza Khosrowabadi3

  • 1Hamadan University of Medical Sciences, Hamadan, Iran. sajjad_farashi@yahoo.com.

Physical and Engineering Sciences in Medicine
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

This study used minimum spanning tree (MST) analysis of electroencephalography (EEG) data to classify emotions. The findings reveal that lower-alpha and gamma brainwave bands are key for distinguishing emotional states.

Keywords:
Arousal-valence spaceElectroencephalographyEmotion recognitionGraph theoryMinimum spanning treePattern recognition

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Area of Science:

  • Neuroscience
  • Computational Psychology
  • Biomedical Engineering

Background:

  • Emotions significantly impact cognitive processes, motivation, decision-making, and social interactions.
  • Emotional states are accompanied by physiological changes detectable through computational methods.
  • Brain functional connectome analysis offers insights into the neural underpinnings of emotions.

Purpose of the Study:

  • To classify emotions using electroencephalography (EEG) data by analyzing changes in the minimum spanning tree (MST) structure of the brain's functional connectome.
  • To identify the most informative frequency bands for emotion classification.
  • To interpret the complexity and dynamics of brain activity during various emotional states.

Main Methods:

  • Applied various connectivity metrics to estimate interactions between different brain regions across different frequency bands.
  • Extracted the MST graph from the functional connectivity matrix.
  • Utilized MST graph features for emotion recognition using EEG data.

Main Results:

  • Achieved 88.28% accuracy in classifying emotions based on arousal levels.
  • Attained 81.25% accuracy in classifying emotions based on valence levels.
  • Demonstrated over 80% accuracy for binary emotion classification within the arousal-valence space quadrants.

Conclusions:

  • The MST approach effectively captures changes in brain complexity and dynamics associated with emotional states.
  • Lower-alpha and gamma frequency bands are identified as containing the primary information for discriminating between emotional states.
  • This method provides a robust framework for computational emotion recognition and understanding neural correlates of emotion.