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Cross-subject EEG emotion recognition using multi-source domain manifold feature selection.

Qingshan She1, Xinsheng Shi1, Feng Fang2

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.

Computers in Biology and Medicine
|April 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-source transfer learning framework to enhance electroencephalogram (EEG) emotion recognition by addressing the cross-subject problem. The proposed method significantly improves accuracy compared to existing domain adaptation techniques.

Keywords:
Affective brain-computer interfaceEmotion recognitionSource domain selection

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Emotion recognition using electroencephalogram (EEG) faces challenges due to inter-subject variability, a problem often addressed by domain adaptation in Affective Brain-Computer Interfaces (aBCI).
  • Traditional domain adaptation methods can suffer from negative transfer when dealing with multiple data sources or single-source transfers.
  • Developing effective cross-subject emotion recognition remains a key challenge in the aBCI field.

Purpose of the Study:

  • To propose a novel multi-source transfer learning framework to improve the performance of EEG-based emotion recognition across different subjects.
  • To mitigate negative transfer issues inherent in traditional domain adaptation techniques.
  • To enhance the accuracy and feasibility of emotion recognition in aBCI systems.

Main Methods:

  • A multi-source transfer learning framework utilizing data distribution similarity analysis (DDSA) for optimal source domain selection.
  • Manifold feature mapping on Grassmann manifold to reduce data drift, followed by minimum redundancy maximum correlation (mRMR) for feature selection.
  • Learning a domain-invariant classifier via structural risk minimization (SRM) and improving performance with a weighted fusion criterion.

Main Results:

  • The proposed framework achieved improved recognition accuracy on both SEED and DEAP datasets compared to conventional methods.
  • Accuracy gains of 6.74% on SEED and 5.34% on DEAP were observed against the MEDA algorithm.
  • The method demonstrated superior performance over TCA, JDA, and other state-of-the-art algorithms, reaching an average accuracy of 86.59% on SEED and 64.40% on DEAP.

Conclusions:

  • The developed multi-source transfer learning framework effectively addresses the cross-subject problem in EEG emotion recognition.
  • The proposed approach offers a more effective and feasible solution compared to existing domain adaptation techniques.
  • This research contributes to advancing the capabilities of Affective Brain-Computer Interfaces through improved emotion recognition.