Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning Article Swipe
Zhibin Jiang
,
Keli Hu
,
Jia Qu
,
Zekang Bian
,
Donghua Yu
,
Jie Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fninf.2025.1559335
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fninf.2025.1559335
Introduction Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain–computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization. Methods To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods. Results and discussion The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fninf.2025.1559335
- https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2025.1559335/pdf
- OA Status
- gold
- References
- 52
- Related Works
- 10
- OpenAlex ID
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- DOI
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https://doi.org/10.3389/fninf.2025.1559335Digital Object Identifier
- Title
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Recognition of MI-EEG signals using extended-LSR-based inductive transfer learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-09Full publication date if available
- Authors
-
Zhibin Jiang, Keli Hu, Jia Qu, Zekang Bian, Donghua Yu, Jie ZhouList of authors in order
- Landing page
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https://doi.org/10.3389/fninf.2025.1559335Publisher landing page
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https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2025.1559335/pdfDirect link to full text PDF
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2025.1559335/pdfDirect OA link when available
- Concepts
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Transfer of learning, Computer science, Electroencephalography, Pattern recognition (psychology), Speech recognition, Artificial intelligence, Psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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