Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion
Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed f...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Haixian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Cham : Springer Nature, 1963, 49(2011), 9 vom: 25. März, Seite 997-1001 |
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| Übergeordnetes Werk: |
volume:49 ; year:2011 ; number:9 ; day:25 ; month:03 ; pages:997-1001 |
| Links: |
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| DOI / URN: |
10.1007/s11517-011-0766-7 |
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| Katalog-ID: |
SPR020509340 |
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| 520 | |a Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. | ||
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10.1007/s11517-011-0766-7 doi (DE-627)SPR020509340 (SPR)s11517-011-0766-7-e DE-627 ger DE-627 rakwb eng Wang, Haixian verfasserin aut Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2011 Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. Common spatial patterns (dpeaa)DE-He213 Brain-computer interfaces (dpeaa)DE-He213 Fisher criterion (dpeaa)DE-He213 Single-trial EEG classification (dpeaa)DE-He213 Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 49(2011), 9 vom: 25. März, Seite 997-1001 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:49 year:2011 number:9 day:25 month:03 pages:997-1001 https://dx.doi.org/10.1007/s11517-011-0766-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 49 2011 9 25 03 997-1001 |
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10.1007/s11517-011-0766-7 doi (DE-627)SPR020509340 (SPR)s11517-011-0766-7-e DE-627 ger DE-627 rakwb eng Wang, Haixian verfasserin aut Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2011 Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. Common spatial patterns (dpeaa)DE-He213 Brain-computer interfaces (dpeaa)DE-He213 Fisher criterion (dpeaa)DE-He213 Single-trial EEG classification (dpeaa)DE-He213 Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 49(2011), 9 vom: 25. März, Seite 997-1001 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:49 year:2011 number:9 day:25 month:03 pages:997-1001 https://dx.doi.org/10.1007/s11517-011-0766-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 49 2011 9 25 03 997-1001 |
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Wang, Haixian |
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Wang, Haixian misc Common spatial patterns misc Brain-computer interfaces misc Fisher criterion misc Single-trial EEG classification Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion |
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Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion Common spatial patterns (dpeaa)DE-He213 Brain-computer interfaces (dpeaa)DE-He213 Fisher criterion (dpeaa)DE-He213 Single-trial EEG classification (dpeaa)DE-He213 |
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misc Common spatial patterns misc Brain-computer interfaces misc Fisher criterion misc Single-trial EEG classification |
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Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion |
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Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion |
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optimizing spatial filters for single-trial eeg classification via a discriminant extension to csp: the fisher criterion |
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Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion |
| abstract |
Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. © International Federation for Medical and Biological Engineering 2011 |
| abstractGer |
Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. © International Federation for Medical and Biological Engineering 2011 |
| abstract_unstemmed |
Abstract In this article, a new spatial filtering approach, called discriminant common spatial patterns (dCSP), is proposed for single-trial EEG classification. Unlike the conventional common spatial patterns (CSP) that is substantially a subspace decomposition technique, dCSP is intently designed for discriminant purpose. The basic idea of dCSP is to construct a Fisher-like criterion that extracts both between-class and within-class discriminant information. The classical CSP only considers separating class means, i.e., between-class scatter, as well as possible. In contrast, dCSP aims to maximize between-class scatter and meanwhile minimize within-class scatter. Computationally, dCSP is formulated as a generalized eigenvalue problem. Experiments on real EEG classification show the effectiveness of the proposed method. © International Federation for Medical and Biological Engineering 2011 |
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Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion |
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https://dx.doi.org/10.1007/s11517-011-0766-7 |
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