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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network

Shiva Asadzadeh1, Tohid Yousefi Rezaii2, Soosan Beheshti3

  • 1Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

Scientific Reports
|June 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain source modeling technique for enhanced emotion recognition from electroencephalography (EEG) signals. The method significantly improves accuracy in classifying emotions, offering a breakthrough for psychiatric disease treatment.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Automatic emotion detection from electroencephalography (EEG) signals is crucial for psychiatric disease treatment.
  • Low spatial resolution of EEG recorders presents a significant challenge for accurate emotion analysis.

Purpose of the Study:

  • To develop a novel method for modeling brain sources from scalp EEG signals to improve emotion recognition accuracy.
  • To overcome the limitations of low spatial resolution in EEG data.

Main Methods:

  • A Bernoulli-Laplace-based Bayesian model was employed to map scalp sensor data to brain sources.
  • Standard low-resolution electromagnetic tomography (sLORETA) was used for initial source signal estimation.
  • A dynamic graph convolutional neural network (DGCNN) was utilized for classifying emotional EEG, with brain sources as graph nodes.

Main Results:

  • The proposed brain source modeling approach significantly enhanced emotion recognition accuracy.
  • Achieved a classification accuracy of 99.25% for positive and negative emotions.
  • Demonstrated an absolute 1-2% improvement over existing methods in both subject-dependent and independent scenarios.

Conclusions:

  • The developed brain source modeling technique effectively addresses the spatial resolution limitations of EEG.
  • This method offers a promising advancement for accurate and reliable emotion recognition in clinical and research settings.
  • The findings highlight the potential of advanced machine learning models integrated with source localization for understanding brain activity related to emotions.