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Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition.

Wei Lu1,2, Xiaobo Zhang2,3, Lingnan Xia1

  • 1Henan High-speed Railway Operation and Maintenance Engineering Research Center, Zhengzhou Railway Vocational and Technical College, Zhengzhou, China.

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PubMed
Summary

A new deep learning model, DSP-EmotionNet, enhances emotion recognition from EEG signals by simultaneously capturing spatial activity and topology features. This approach improves cross-subject EEG emotion recognition accuracy, outperforming existing methods.

Keywords:
affective computingconvolutional neural networkdomain adaptationelectroencephalographyemotion recognitiongraph attention network

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Area of Science:

  • Affective computing
  • Neuroscience
  • Machine learning

Background:

  • Emotion recognition is vital in affective computing, with EEG-based deep learning showing promise.
  • Current deep learning models struggle to integrate spatial activity and topology features from EEG signals.
  • Addressing this gap is crucial for advancing EEG-based emotion recognition.

Purpose of the Study:

  • To propose a novel deep learning network, DSP-EmotionNet, for cross-subject EEG emotion recognition.
  • To enhance the simultaneous capture of spatial activity and topology features from EEG signals.
  • To improve the accuracy and robustness of EEG-based emotion recognition models.

Main Methods:

  • A spatial activity topological feature extractor module (SATFEM) was designed to extract both spatial activity and topology features.
  • DSP-EmotionNet utilizes SATFEM as its core feature extractor.
  • The model was evaluated on cross-subject EEG emotion recognition tasks.

Main Results:

  • DSP-EmotionNet significantly improved accuracy in cross-subject EEG emotion recognition.
  • The model achieved an average accuracy of 82.5% on the SEED dataset.
  • The model achieved an average accuracy of 65.9% on the SEED-IV dataset, surpassing state-of-the-art methods.

Conclusions:

  • DSP-EmotionNet effectively addresses the limitations of existing models in capturing complex EEG signal features.
  • The proposed method demonstrates superior performance in cross-subject emotion recognition.
  • This work advances the field of EEG-based emotion recognition with practical implications for affective computing.