Local Polynomial Estimate of Surface Laplacian
Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new fe...
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| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1999 |
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| Umfang: |
11 |
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| Reproduktion: |
Springer Online Journal Archives 1860-2002 |
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| Übergeordnetes Werk: |
in: Brain topography - 1988, 12(1999) vom: Jan., Seite 19-29 |
| Übergeordnetes Werk: |
volume:12 ; year:1999 ; month:01 ; pages:19-29 ; extent:11 |
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NLEJ196720958 |
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| 245 | 1 | 0 | |a Local Polynomial Estimate of Surface Laplacian |
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| 520 | |a Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. | ||
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| 700 | 1 | |a Wang, Kongming |4 oth | |
| 700 | 1 | |a Begleiter, Henri |4 oth | |
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(DE-627)NLEJ196720958 DE-627 ger DE-627 rakwb eng Local Polynomial Estimate of Surface Laplacian 1999 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. Springer Online Journal Archives 1860-2002 Wang, Kongming oth Begleiter, Henri oth in Brain topography 1988 12(1999) vom: Jan., Seite 19-29 (DE-627)NLEJ188986987 (DE-600)2015003-9 1573-6792 nnns volume:12 year:1999 month:01 pages:19-29 extent:11 http://dx.doi.org/10.1023/A:1022225522447 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 12 1999 1 19-29 11 |
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(DE-627)NLEJ196720958 DE-627 ger DE-627 rakwb eng Local Polynomial Estimate of Surface Laplacian 1999 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. Springer Online Journal Archives 1860-2002 Wang, Kongming oth Begleiter, Henri oth in Brain topography 1988 12(1999) vom: Jan., Seite 19-29 (DE-627)NLEJ188986987 (DE-600)2015003-9 1573-6792 nnns volume:12 year:1999 month:01 pages:19-29 extent:11 http://dx.doi.org/10.1023/A:1022225522447 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 12 1999 1 19-29 11 |
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(DE-627)NLEJ196720958 DE-627 ger DE-627 rakwb eng Local Polynomial Estimate of Surface Laplacian 1999 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. Springer Online Journal Archives 1860-2002 Wang, Kongming oth Begleiter, Henri oth in Brain topography 1988 12(1999) vom: Jan., Seite 19-29 (DE-627)NLEJ188986987 (DE-600)2015003-9 1573-6792 nnns volume:12 year:1999 month:01 pages:19-29 extent:11 http://dx.doi.org/10.1023/A:1022225522447 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 12 1999 1 19-29 11 |
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(DE-627)NLEJ196720958 DE-627 ger DE-627 rakwb eng Local Polynomial Estimate of Surface Laplacian 1999 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. Springer Online Journal Archives 1860-2002 Wang, Kongming oth Begleiter, Henri oth in Brain topography 1988 12(1999) vom: Jan., Seite 19-29 (DE-627)NLEJ188986987 (DE-600)2015003-9 1573-6792 nnns volume:12 year:1999 month:01 pages:19-29 extent:11 http://dx.doi.org/10.1023/A:1022225522447 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 12 1999 1 19-29 11 |
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(DE-627)NLEJ196720958 DE-627 ger DE-627 rakwb eng Local Polynomial Estimate of Surface Laplacian 1999 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. Springer Online Journal Archives 1860-2002 Wang, Kongming oth Begleiter, Henri oth in Brain topography 1988 12(1999) vom: Jan., Seite 19-29 (DE-627)NLEJ188986987 (DE-600)2015003-9 1573-6792 nnns volume:12 year:1999 month:01 pages:19-29 extent:11 http://dx.doi.org/10.1023/A:1022225522447 GBV_USEFLAG_U ZDB-1-SOJ GBV_NL_ARTICLE AR 12 1999 1 19-29 11 |
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Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. |
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Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. |
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Abstract This paper describes a method for estimating the surface Laplacian of brain potentials. The method consists of two steps: local surface approximation by its tangent plane and local polynomial fitting. Compared to previous methods for estimating surface Laplacian, this method has some new features. First, it can estimate the surface Laplacian at any point of the scalp, including the locations of the peripheral electrodes. Secondly, it estimates the brain potential and the surface Laplacian at any point simultaneously. This reduces the risk of error propagation, which occurs when the brain potential is interpolated first and the surface Laplacian is then computed based on the interpolated brain potential. Finally, the method automatically adapts to noisy data by using more or less measurements at neighboring electrodes based on estimated noise level. Simulations suggest that this method is effective. Application to event-related potentials are also presented. |
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