Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed...
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| Veröffentlicht in: | IEEE transactions on biomedical engineering Jg. 58; H. 2; S. 355 - 362 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY
IEEE
01.02.2011
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 0018-9294, 1558-2531, 1558-2531 |
| Online-Zugang: | Volltext |
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| Abstract | One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper. |
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| AbstractList | One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper. One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper.One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper. One of the most popular feature extraction algorithms for Brain-Computer Interfaces (BCI) is the Common Spatial Patterns (CSP) algorithm. Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, some groups have recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to perform such a CSP regularization. We then present a mini-review of existing Regularized CSP (RCSP) algorithms, and describe how to cast them in this framework. We also propose 4 new RCSP algorithms. Finally, we compare the performances of 11 different RCSP algorithms (including these 4 new ones and the original CSP), on EEG data from 17 subjects, from BCI competition data sets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms on these data were the CSP with Tikhonov Regularization and Weighted Tikhonov Regularization, both newly proposed in this paper. |
| Author | Guan, Cuntai Lotte, Fabien |
| Author_xml | – sequence: 1 givenname: Fabien surname: Lotte fullname: Lotte, Fabien email: fprlotte@i2r.a-star.edu.sg organization: Brain-Computer Interface Laboratory , Signal Processing Department, Institute for Infocomm Research, Singapore – sequence: 2 givenname: Cuntai surname: Guan fullname: Guan, Cuntai email: ctguan@i2r.a-star.edu.sg organization: Institute for Infocomm Research , Singapore |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23853177$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/20889426$$D View this record in MEDLINE/PubMed https://inria.hal.science/inria-00476820$$DView record in HAL |
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| Cites_doi | 10.1016/S0047-259X(03)00096-4 10.1109/CVPR.2007.383054 10.1109/TBME.2008.921154 10.1007/978-1-4757-3264-1 10.1109/ICPR.2010.904 10.1109/86.895946 10.1016/j.neunet.2009.06.003 10.1109/5.939829 10.1109/MSP.2008.4408441 10.1109/TBME.2004.826692 10.1109/ICASSP.2010.5495183 10.1088/1741-2560/4/2/R01 10.1109/TBME.2008.2009768 10.1109/ICPR.1994.576920 10.1016/j.neunet.2009.07.020 10.1016/j.neuroimage.2009.07.045 10.1109/TBME.2004.827088 10.1088/1741-2560/3/3/003 10.1109/LSP.2009.2022557 10.1088/1741-2560/2/4/L02 10.1109/TNSRE.2006.875642 |
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| References_xml | – ident: ref16 doi: 10.1016/S0047-259X(03)00096-4 – ident: ref21 doi: 10.1109/CVPR.2007.383054 – start-page: 17 year: 2010 ident: ref31 article-title: multitask learning for brain-computer interfaces publication-title: Proc AISTATS – ident: ref30 doi: 10.1109/TBME.2008.921154 – start-page: 417 year: 2008 ident: ref15 article-title: sparse spatial filter optimization for eeg channel reduction in brain-computer interface publication-title: Proc ICASSP – ident: ref27 doi: 10.1007/978-1-4757-3264-1 – ident: ref12 doi: 10.1109/ICPR.2010.904 – year: 2008 ident: ref9 publication-title: NIPS 20 – ident: ref3 doi: 10.1109/86.895946 – ident: ref28 doi: 10.1016/j.neunet.2009.06.003 – year: 0 ident: ref26 – year: 2001 ident: ref25 publication-title: Applied Nonparametric Statistical Methods – start-page: 6599 year: 2009 ident: ref10 article-title: regularized common spatial patterns with generic learning for eeg signal classification publication-title: Proc EMBC – ident: ref1 doi: 10.1109/5.939829 – ident: ref4 doi: 10.1109/MSP.2008.4408441 – ident: ref5 doi: 10.1109/TBME.2004.826692 – ident: ref13 doi: 10.1109/ICASSP.2010.5495183 – year: 2009 ident: ref29 article-title: an efficient p300-based brain-computer interface with minimal calibration time publication-title: Proc AMD-NIPS – ident: ref2 doi: 10.1088/1741-2560/4/2/R01 – ident: ref8 doi: 10.1109/TBME.2008.2009768 – year: 2008 ident: ref7 publication-title: Robustness of the common spatial patterns algorithm in the BCI-pipeline – ident: ref18 doi: 10.1109/ICPR.1994.576920 – volume: 4 start-page: 1624 year: 1963 ident: ref19 article-title: regularization of incorrectly posed problems publication-title: Soviet Math – year: 2006 ident: ref14 article-title: Regularised CSP for sensor selection in BCI publication-title: 3rd Int BCI workshop – ident: ref17 doi: 10.1016/j.neunet.2009.07.020 – ident: ref32 doi: 10.1016/j.neuroimage.2009.07.045 – ident: ref22 doi: 10.1109/TBME.2004.827088 – ident: ref24 doi: 10.1088/1741-2560/3/3/003 – ident: ref11 doi: 10.1109/LSP.2009.2022557 – ident: ref23 doi: 10.1088/1741-2560/2/4/L02 – ident: ref6 doi: 10.1109/TNSRE.2006.875642 – start-page: 2107 year: 2009 ident: ref20 article-title: boosting with spatial regularization publication-title: Proc NIPS |
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| Snippet | One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and... One of the most popular feature extraction algorithms for Brain-Computer Interfaces (BCI) is the Common Spatial Patterns (CSP) algorithm. Despite its known... |
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| SubjectTerms | Algorithm design and analysis Algorithms Applied sciences Artificial Intelligence Biological and medical sciences Biotechnology Brain-computer interfaces (BCI) common spatial patterns (CSP) Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Computerized, statistical medical data processing and models in biomedicine Covariance matrix Eigenvalues and eigenfunctions Electrodes Electrodiagnosis. Electric activity recording Electroencephalography electroencephalography (EEG) Electroencephalography - methods Engineering Sciences Exact sciences and technology Humans Investigative techniques, diagnostic techniques (general aspects) Life Sciences Man-Machine Systems Medical management aid. Diagnosis aid Medical sciences Models, Neurological Nervous system Noise Pattern Recognition, Automated - methods Regression Analysis regularization Signal and Image Processing Signal Processing, Computer-Assisted Software subject-to-subject transfer Training User-Computer Interface |
| Title | Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms |
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