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Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.

Jing Wang1, Xiaojun Ning1, Wei Xu1

  • 1Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network for electroencephalography (EEG) emotion recognition, improving accuracy by leveraging multi-subject data. The method effectively extracts subject-invariant features for more robust emotion detection.

Keywords:
Domain adaptationDomain selectionElectroencephalogram (EEG)Emotion recognitionGraph neural network

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Affective brain-computer interfaces (BCIs) are crucial for emotional human-computer interaction.
  • Individual differences in electroencephalography (EEG) data present a significant challenge for emotion recognition.

Purpose of the Study:

  • To develop a method for robust EEG emotion recognition that overcomes subject-specific variations.
  • To enhance the utilization of multi-subject data for improved target subject emotion recognition.

Main Methods:

  • Proposed a Multi-source Selective Graph Domain Adaptation Network (MSGDAN).
  • MSGDAN extracts subject-invariant representations by distinguishing public and individual information.
  • Employed a dynamic graph network and graph domain adaptation to capture and ensure invariance of brain's functional connectivity and regional states.

Main Results:

  • Achieved superior classification performance in cross-subject emotion recognition experiments.
  • Validated on SEED, SEED-IV, and DEAP datasets, demonstrating robust performance.

Conclusions:

  • The MSGDAN effectively addresses challenges in EEG emotion recognition caused by individual differences.
  • The proposed network enhances the fusion of multi-source subject data for more accurate emotion detection.