Global Adaptive Transformer for Cross-Subject Enhanced EEG Classification Article Swipe
Yonghao Song
,
Qingqing Zheng
,
Qiong Wang
,
Xiaorong Gao
,
Pheng‐Ann Heng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2023.3285309
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnsre.2023.3285309
Due to the individual difference, EEG signals from other subjects (source) can hardly be used to decode the mental intentions of the target subject. Although transfer learning methods have shown promising results, they still suffer from poor feature representation or neglect long-range dependencies. In light of these limitations, we propose Global Adaptive Transformer (GAT), an domain adaptation method to utilize source data for cross-subject enhancement. Our method uses parallel convolution to capture temporal and spatial features first. Then, we employ a novel attention-based adaptor that implicitly transfers source features to the target domain, emphasizing the global correlation of EEG features. We also use a discriminator to explicitly drive the reduction of marginal distribution discrepancy by learning against the feature extractor and the adaptor. Besides, an adaptive center loss is designed to align the conditional distribution. With the aligned source and target features, a classifier can be optimized to decode EEG signals. Experiments on two widely used EEG datasets demonstrate that our method outperforms state-of-the-art methods, primarily due to the effectiveness of the adaptor. These results indicate that GAT has good potential to enhance the practicality of BCI.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsre.2023.3285309
- https://ieeexplore.ieee.org/ielx7/7333/10031624/10149036.pdf
- OA Status
- diamond
- Cited By
- 36
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380355219
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- OpenAlex ID
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https://openalex.org/W4380355219Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnsre.2023.3285309Digital Object Identifier
- Title
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Global Adaptive Transformer for Cross-Subject Enhanced EEG ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Yonghao Song, Qingqing Zheng, Qiong Wang, Xiaorong Gao, Pheng‐Ann HengList of authors in order
- Landing page
-
https://doi.org/10.1109/tnsre.2023.3285309Publisher landing page
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https://ieeexplore.ieee.org/ielx7/7333/10031624/10149036.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/7333/10031624/10149036.pdfDirect OA link when available
- Concepts
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Electroencephalography, Transformer, Computer science, Artificial intelligence, Speech recognition, Pattern recognition (psychology), Psychology, Engineering, Neuroscience, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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36Total citation count in OpenAlex
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2025: 15, 2024: 18, 2023: 3Per-year citation counts (last 5 years)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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