Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning
IntroductionMotor 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...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1559335/full |
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| author | Zhibin Jiang Zhibin Jiang Keli Hu Keli Hu Jia Qu Zekang Bian Zekang Bian Donghua Yu Donghua Yu Jie Zhou Jie Zhou |
| author_facet | Zhibin Jiang Zhibin Jiang Keli Hu Keli Hu Jia Qu Zekang Bian Zekang Bian Donghua Yu Donghua Yu Jie Zhou Jie Zhou |
| author_sort | Zhibin Jiang |
| collection | DOAJ |
| description | IntroductionMotor 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.MethodsTo 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 discussionThe 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. |
| format | Article |
| id | doaj-art-893202873baa4b1386eaceea4630a0bf |
| institution | OA Journals |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-893202873baa4b1386eaceea4630a0bf2025-08-20T02:16:29ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-04-011910.3389/fninf.2025.15593351559335Recognition of MI-EEG signals using extended-LSR-based inductive transfer learningZhibin Jiang0Zhibin Jiang1Keli Hu2Keli Hu3Jia Qu4Zekang Bian5Zekang Bian6Donghua Yu7Donghua Yu8Jie Zhou9Jie Zhou10Department of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaInstitute of Artificial Intelligence, Shaoxing University, Shaoxing, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaInformation Technology R&D Innovation Center of Peking University, Shaoxing, ChinaDepartment of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, ChinaDepartment of AI & Computer Science, Jiangnan University, Wuxi, ChinaDepartment of Taihu Jiangsu Key Construction Lab of IoT Application Technologies, Wuxi, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaInstitute of Artificial Intelligence, Shaoxing University, Shaoxing, ChinaDepartment of Computer Science and Engineering, Shaoxing University, Shaoxing, ChinaInstitute of Artificial Intelligence, Shaoxing University, Shaoxing, ChinaIntroductionMotor 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.MethodsTo 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 discussionThe 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.https://www.frontiersin.org/articles/10.3389/fninf.2025.1559335/fullmotor imageryEEGbrain-computer interfaceLSRinductive transfer learning |
| spellingShingle | Zhibin Jiang Zhibin Jiang Keli Hu Keli Hu Jia Qu Zekang Bian Zekang Bian Donghua Yu Donghua Yu Jie Zhou Jie Zhou Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning Frontiers in Neuroinformatics motor imagery EEG brain-computer interface LSR inductive transfer learning |
| title | Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning |
| title_full | Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning |
| title_fullStr | Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning |
| title_full_unstemmed | Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning |
| title_short | Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning |
| title_sort | recognition of mi eeg signals using extended lsr based inductive transfer learning |
| topic | motor imagery EEG brain-computer interface LSR inductive transfer learning |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1559335/full |
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