Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach
Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Met...
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| Vydáno v: | IEEE transactions on biomedical engineering Ročník 67; číslo 2; s. 399 - 410 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0018-9294, 1558-2531, 1558-2531 |
| On-line přístup: | Získat plný text |
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| Abstract | Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. |
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| AbstractList | This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data?OBJECTIVEThis paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data?We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject.METHODSWe propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject.Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment.RESULTSBoth offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment.The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs.CONCLUSIONThe proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs.Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.SIGNIFICANCEOur proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods : We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results : Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion : The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance : Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs. |
| Author | Wu, Dongrui He, He |
| Author_xml | – sequence: 1 givenname: He orcidid: 0000-0002-9118-2449 surname: He fullname: He, He email: hehe91@hust.edu.cn organization: Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, and also with the School of Artificial Intelligence and AutomationHuazhong University of Science and Technology – sequence: 2 givenname: Dongrui orcidid: 0000-0002-7153-9703 surname: Wu fullname: Wu, Dongrui email: drwu@hust.edu.cn organization: Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, and also with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31034407$$D View this record in MEDLINE/PubMed |
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| CODEN | IEBEAX |
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| Cites_doi | 10.1161/01.CIR.101.23.e215 10.3390/s120201211 10.1109/THMS.2016.2608931 10.1145/1961189.1961199 10.1109/ICASSP.2010.5495183 10.1109/SMC.2016.7844284 10.1109/MCI.2015.2501545 10.1109/TFUZZ.2017.2688423 10.1109/JPROC.2012.2184830 10.1016/S1388-2457(98)00038-8 10.1371/journal.pone.0178498 10.1109/TKDE.2009.191 10.1007/978-3-319-70096-0_83 10.1109/MSP.2008.4408441 10.1007/978-3-540-27816-0_8 10.1109/MC.2012.107 10.1007/BF01129656 10.1109/TNSRE.2016.2627016 10.1109/TBME.2017.2742541 10.1016/j.neuroimage.2007.01.051 10.1109/TNSRE.2016.2544108 10.1016/S0378-3758(00)00115-4 10.1109/LSP.2009.2022557 10.1109/TBME.2011.2172210 10.1109/TBME.2013.2253608 10.1109/SMC.2017.8122610 10.1109/MC.2008.432 10.1109/TNSRE.2017.2699784 10.1109/TFUZZ.2016.2633379 10.1016/S1388-2457(02)00057-3 10.1088/1741-2560/11/3/035005 10.1109/TBME.2009.2012869 10.2307/2283970 10.1109/86.895946 |
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| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
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| Snippet | Objective: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with... This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual... Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with... |
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| SubjectTerms | Algorithms Alignment Brain Brain-computer interface Brain-Computer Interfaces Classification Computational neuroscience Computer Simulation Covariance matrices data alignment Data processing Databases, Factual EEG Electroencephalography Electroencephalography - classification Euclidean geometry Euclidean space Event-related potentials Evoked Potentials - physiology Feature extraction Humans Image classification Imagination - physiology Interfaces Learning algorithms Machine Learning Machine learning algorithms Mental task performance Microsoft Windows Riemannian geometry Signal processing Signal Processing, Computer-Assisted Task analysis Transfer learning |
| Title | Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach |
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