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Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual

Jingxia Chen1, Chongdan Min1, Changhao Wang1

  • 1School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an, China.

Frontiers in Neuroscience
|July 11, 2022
PubMed
Summary

This study introduces a novel brain connectivity and domain adaptive residual convolutional network (BC-DA-RCNN) for electroencephalograph (EEG) emotion recognition. The method achieves high accuracy in subject-dependent and independent emotion classification.

Keywords:
EEGbrain connectivitydomain adaptativeemotion recognitionresidual convolution

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Affective Computing

Background:

  • Accurate electroencephalograph (EEG) emotion recognition is crucial for advancing affective brain-computer interfaces.
  • Extracting high-level, discriminative emotional features from EEG signals remains a key challenge.
  • Existing methods often struggle with inter-subject variability and domain shifts.

Purpose of the Study:

  • To propose a novel end-to-end emotion recognition method using brain connectivity (BC) features and a domain adaptive residual convolutional network (DA-RCNN).
  • To effectively extract spatial connectivity information related to emotional states from EEG signals.
  • To achieve accurate emotion recognition within and across subjects by reducing domain offset and strengthening common features.

Main Methods:

  • Representing brain connectivity information using global brain network connectivity matrices.
  • Employing a domain adaptive residual convolutional network (DA-RCNN) for feature extraction.
  • Introducing domain adaptation techniques to mitigate differences between subjects' EEG data.

Main Results:

  • Achieved 95.15% accuracy for subject-dependent binary emotion classification in valence.
  • Reached 88.28% accuracy for subject-independent binary emotion classification in valence on the DEAP dataset.
  • Outperformed all benchmark methods in terms of accuracy, complexity, generalization, and domain robustness.

Conclusions:

  • The proposed BC-DA-RCNN method demonstrates superior performance in EEG-based emotion recognition.
  • The approach effectively extracts discriminative emotional features and handles inter-subject variability.
  • This work provides a strong foundation for developing high-performance affective brain-computer interface applications.