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[Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy].

Ziliang Cai^1,2, Miaomiao Guo^1,2, Xinsheng Yang^1,2

¹State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|June 28, 2021

Summary

This summary is machine-generated.

This study introduces a novel domain adaptation method for affective brain-computer interfaces (aBCIs) using electroencephalogram (EEG) data. The new approach significantly improves emotion recognition accuracy across different subjects and sessions.

Keywords:

affective brain-computer interfaces cross-subject affective models electroencephalogram transfer learning

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Area of Science:

Neuroscience
Machine Learning
Human-Computer Interaction

Background:

Affective brain-computer interfaces (aBCIs) leverage electroencephalogram (EEG) for emotion recognition.
EEG's non-stationary nature and individual variability hinder model generalization across subjects and sessions.
Existing models struggle with reliable emotion recognition in diverse real-world scenarios.

Purpose of the Study:

To develop a domain adaptation method for robust emotion recognition in aBCIs.
To enhance the generalization capability of EEG-based emotion recognition models.
To address challenges posed by inter-subject and inter-session variability in EEG data.

Main Methods:

Proposed a novel Maximum Classifier Discrepancy domain adaptation (MCD_DA) method.
Utilized a neural network architecture with a shallow feature extractor.
Employed adversarial training to achieve domain-invariant feature representation and joint distribution adaptation.

Main Results:

The MCD_DA method achieved an average classification accuracy of 88.33%.
This represents a significant improvement over the traditional general classifier's accuracy of 58.23%.
Demonstrated enhanced generalization of the emotion recognition model.

Conclusions:

The proposed MCD_DA method effectively improves emotion recognition generalization in aBCIs.
This approach offers a promising solution for practical applications of affective brain-computer interfaces.
Provides a new methodology for overcoming domain shift challenges in EEG-based emotion recognition.