Conditional adversarial domain adaptation based on sample weight

Dan Wang; Junhui Zhu; Meng Xu; Jiaming Chen

doi:10.1117/12.3006071

10 October 2023 Conditional adversarial domain adaptation based on sample weight

Dan Wang, Junhui Zhu, Meng Xu, Jiaming Chen

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992K (2023) https://doi.org/10.1117/12.3006071
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

To address the problem that some source domain samples that are difficult to transfer disturb the target domain data distribution due to the difference in transfer value between different motor imagery electroencephalogram (MI-EEG) sample data, and that the model has poor feature extraction and classification performance when adapting to different motor imagery datasets, this paper improves the conditional domain adversarial network (CDAN) method introduced by domain generalization technology, and proposes a conditional domain adaptation network based on sample weight (SW-CDAN) method. This method makes the entropy output by the domain discriminator as the sample weight, which is used to adjust the classification loss during the model training process, so that the model can extract transferable features from the common features of the data, thereby enhancing the model’s category prediction ability and model generalization ability. The experimental results show that the SW-CDAN method can effectively improve the classification performance and model generalization ability of motor imagery EEG signals, so that even when facing a small amount of motor imagery EEG signals with low effective components, it can still maintain a high classification accuracy. The SWCDAN method achieves relatively high classification accuracy on BCI Competition IV 2a dataset, which is about 1.87% higher than CDAN method respectively.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dan Wang, Junhui Zhu, Meng Xu, and Jiaming Chen "Conditional adversarial domain adaptation based on sample weight", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992K (10 October 2023); https://doi.org/10.1117/12.3006071

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KEYWORDS

Data modeling

Statistical modeling

Electroencephalography

Feature extraction

Performance modeling

Education and training

Image classification

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