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Multiple-source distribution deep adaptive feature norm network for EEG emotion recognition.

Lei Zhu1, Fei Yu1, Wangpan Ding1

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310000 China.

Cognitive Neurodynamics
|November 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning network for Electroencephalogram (EEG) emotion recognition. The method effectively reduces domain differences, significantly improving emotion recognition accuracy in human-computer interaction.

Keywords:
Domain adaptationEEGEmotion recognitionTransfer learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Electroencephalogram (EEG) emotion recognition is crucial for advancing human-computer interaction.
  • Domain adaptive methods in transfer learning aim to create generalizable emotion recognition models by addressing domain differences.
  • Effectively reducing domain differences remains a significant challenge in EEG-based emotion recognition.

Purpose of the Study:

  • To propose a novel deep learning network, the Multiple-Source Distribution Deep Adaptive Feature Norm Network, for EEG emotion recognition.
  • To enhance the transferability of task-specific features for reducing domain differences.
  • To improve the overall accuracy of EEG emotion recognition systems.

Main Methods:

  • A three-layer network topology incorporating Adaptive Feature Norm for self-supervised adjustment between layers.
  • A multiple-kernel selection approach integrated with mean embedding matching for domain adaptation.
  • Utilizing the SEED and SEED-IV datasets for cross-subject and cross-session experiments.

Main Results:

  • The proposed network achieved state-of-the-art classification performance on both SEED and SEED-IV datasets.
  • Achieved 85.01% cross-subject and 91.93% cross-session accuracy on the SEED dataset.
  • Obtained 58.81% cross-subject and 59.51% cross-session accuracy on the SEED-IV dataset.

Conclusions:

  • The developed method effectively reduces domain differences in EEG data.
  • The proposed network significantly improves the accuracy of EEG-based emotion recognition.
  • This approach holds promise for more robust and user-friendly human-computer interaction systems.