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Related Experiment Video

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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram.

Zhen Wei1, Hongwei Li1, Lin Ma1

  • 1School of Computer Science and Technology, Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Frontiers in Neuroscience
|March 29, 2024
PubMed
Summary

This study introduces a novel microstate analysis method for emotional electroencephalogram (EEG) signals, demonstrating its effectiveness in emotion recognition and revealing neurophysiological insights into emotional processing.

Keywords:
affective computingelectroencephalogramemotion recognitionevoked emotionsmicrostate analysis

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Electroencephalogram (EEG) microstate analysis is crucial for understanding brain dynamics.
  • Existing research primarily focuses on resting-state EEG, with limited exploration of emotional states.
  • Investigating emotional EEG using microstate analysis can uncover unique temporal and spatial patterns.

Purpose of the Study:

  • To explore temporal and spatial EEG patterns during emotional states.
  • To determine the neurophysiological significance of microstates in emotion processing.
  • To assess the feasibility and effectiveness of microstate analysis for EEG-based emotion recognition.

Main Methods:

  • Proposed a KLGEV-criterion-based method for adaptive microstate identification in emotional EEG.
  • Extracted temporal and spatial microstate features for emotion recognition.
  • Evaluated the method on the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP).

Main Results:

  • Identified 10 microstates in SEED, achieving 70.38% accuracy in subject-dependent emotion recognition using AutoGluon.
  • Identified 9 microstates in DEAP, with competitive accuracies of 74.33% for arousal and 75.49% for valence.
  • Found significant correlations between specific microstates (C, D, B) and arousal/valence ratings, enhancing interpretability.

Conclusions:

  • The KLGEV-criterion-based method effectively analyzes emotional EEG signals.
  • Microstate features show promise for improving EEG-based emotion recognition.
  • The study expands the understanding of EEG microstate interpretability in emotional contexts.