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Dynamic domain adaptive EEG emotion recognition based on multi-source selection.

Zhongmin Wang1,2,3, Mengxuan Zhao1

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.

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Summary
This summary is machine-generated.

This study introduces a novel dynamic domain-adaptive method for electroencephalogram (EEG) emotion recognition. The approach effectively addresses individual variations, improving cross-subject emotion recognition accuracy.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Emotion recognition using electroencephalogram (EEG) is a significant research area.
  • Individual variations in EEG signals pose challenges for cross-subject emotion recognition.
  • Existing methods struggle to generalize across different subjects due to domain discrepancies.

Purpose of the Study:

  • To propose a dynamic domain-adaptive EEG emotion recognition method using multi-source selection.
  • To mitigate inter-domain discrepancies by considering global and local subdomain differences.
  • To enhance the accuracy and robustness of cross-subject emotion recognition.

Main Methods:

  • A multi-source selection strategy filters relevant subject domains.
  • Extraction of common and domain-specific features from source and target domains.
  • Dynamic domain adaptation adjusts focus from global to local distribution differences during training.

Main Results:

  • Achieved 89.76% and 65.28% cross-subject accuracy on SEED and SEED-IV datasets.
  • Achieved 91.63% and 67.83% cross-session accuracy on SEED and SEED-IV datasets.
  • Demonstrated significant improvement over existing methods in cross-subject scenarios.

Conclusions:

  • The proposed dynamic domain-adaptive method effectively addresses individual variations in EEG signals.
  • The approach shows high efficacy in cross-subject and cross-session emotion recognition tasks.
  • This work advances the field of reliable EEG-based emotion recognition.