Decomposition of Neurological Multivariate Time Series by State Space Modelling
Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Galka, Andreas [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© Society for Mathematical Biology 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Bulletin of mathematical biology - Springer-Verlag, 1973, 73(2010), 2 vom: 04. Sept., Seite 285-324 |
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| Übergeordnetes Werk: |
volume:73 ; year:2010 ; number:2 ; day:04 ; month:09 ; pages:285-324 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11538-010-9563-y |
|---|
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OLC2087084041 |
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| 520 | |a Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. | ||
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10.1007/s11538-010-9563-y doi (DE-627)OLC2087084041 (DE-He213)s11538-010-9563-y-p DE-627 ger DE-627 rakwb eng 570 510 VZ 12 ssgn BIODIV DE-30 fid 42.00 bkl Galka, Andreas verfasserin aut Decomposition of Neurological Multivariate Time Series by State Space Modelling 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Mathematical Biology 2010 Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. Time series analysis Kalman filtering Independent Component Analysis Artifact removal Electroencephalogram EEG/FMRI fusion Wong, Kin Foon Kevin aut Ozaki, Tohru aut Muhle, Hiltrud aut Stephani, Ulrich aut Siniatchkin, Michael aut Enthalten in Bulletin of mathematical biology Springer-Verlag, 1973 73(2010), 2 vom: 04. Sept., Seite 285-324 (DE-627)129391719 (DE-600)184905-0 (DE-576)014776863 0092-8240 nnns volume:73 year:2010 number:2 day:04 month:09 pages:285-324 https://doi.org/10.1007/s11538-010-9563-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2010 GBV_ILN_2088 GBV_ILN_4012 GBV_ILN_4082 42.00 VZ AR 73 2010 2 04 09 285-324 |
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10.1007/s11538-010-9563-y doi (DE-627)OLC2087084041 (DE-He213)s11538-010-9563-y-p DE-627 ger DE-627 rakwb eng 570 510 VZ 12 ssgn BIODIV DE-30 fid 42.00 bkl Galka, Andreas verfasserin aut Decomposition of Neurological Multivariate Time Series by State Space Modelling 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Mathematical Biology 2010 Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. Time series analysis Kalman filtering Independent Component Analysis Artifact removal Electroencephalogram EEG/FMRI fusion Wong, Kin Foon Kevin aut Ozaki, Tohru aut Muhle, Hiltrud aut Stephani, Ulrich aut Siniatchkin, Michael aut Enthalten in Bulletin of mathematical biology Springer-Verlag, 1973 73(2010), 2 vom: 04. Sept., Seite 285-324 (DE-627)129391719 (DE-600)184905-0 (DE-576)014776863 0092-8240 nnns volume:73 year:2010 number:2 day:04 month:09 pages:285-324 https://doi.org/10.1007/s11538-010-9563-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2010 GBV_ILN_2088 GBV_ILN_4012 GBV_ILN_4082 42.00 VZ AR 73 2010 2 04 09 285-324 |
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10.1007/s11538-010-9563-y doi (DE-627)OLC2087084041 (DE-He213)s11538-010-9563-y-p DE-627 ger DE-627 rakwb eng 570 510 VZ 12 ssgn BIODIV DE-30 fid 42.00 bkl Galka, Andreas verfasserin aut Decomposition of Neurological Multivariate Time Series by State Space Modelling 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Mathematical Biology 2010 Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. Time series analysis Kalman filtering Independent Component Analysis Artifact removal Electroencephalogram EEG/FMRI fusion Wong, Kin Foon Kevin aut Ozaki, Tohru aut Muhle, Hiltrud aut Stephani, Ulrich aut Siniatchkin, Michael aut Enthalten in Bulletin of mathematical biology Springer-Verlag, 1973 73(2010), 2 vom: 04. Sept., Seite 285-324 (DE-627)129391719 (DE-600)184905-0 (DE-576)014776863 0092-8240 nnns volume:73 year:2010 number:2 day:04 month:09 pages:285-324 https://doi.org/10.1007/s11538-010-9563-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2010 GBV_ILN_2088 GBV_ILN_4012 GBV_ILN_4082 42.00 VZ AR 73 2010 2 04 09 285-324 |
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10.1007/s11538-010-9563-y doi (DE-627)OLC2087084041 (DE-He213)s11538-010-9563-y-p DE-627 ger DE-627 rakwb eng 570 510 VZ 12 ssgn BIODIV DE-30 fid 42.00 bkl Galka, Andreas verfasserin aut Decomposition of Neurological Multivariate Time Series by State Space Modelling 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Mathematical Biology 2010 Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. Time series analysis Kalman filtering Independent Component Analysis Artifact removal Electroencephalogram EEG/FMRI fusion Wong, Kin Foon Kevin aut Ozaki, Tohru aut Muhle, Hiltrud aut Stephani, Ulrich aut Siniatchkin, Michael aut Enthalten in Bulletin of mathematical biology Springer-Verlag, 1973 73(2010), 2 vom: 04. Sept., Seite 285-324 (DE-627)129391719 (DE-600)184905-0 (DE-576)014776863 0092-8240 nnns volume:73 year:2010 number:2 day:04 month:09 pages:285-324 https://doi.org/10.1007/s11538-010-9563-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2010 GBV_ILN_2088 GBV_ILN_4012 GBV_ILN_4082 42.00 VZ AR 73 2010 2 04 09 285-324 |
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10.1007/s11538-010-9563-y doi (DE-627)OLC2087084041 (DE-He213)s11538-010-9563-y-p DE-627 ger DE-627 rakwb eng 570 510 VZ 12 ssgn BIODIV DE-30 fid 42.00 bkl Galka, Andreas verfasserin aut Decomposition of Neurological Multivariate Time Series by State Space Modelling 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Society for Mathematical Biology 2010 Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. Time series analysis Kalman filtering Independent Component Analysis Artifact removal Electroencephalogram EEG/FMRI fusion Wong, Kin Foon Kevin aut Ozaki, Tohru aut Muhle, Hiltrud aut Stephani, Ulrich aut Siniatchkin, Michael aut Enthalten in Bulletin of mathematical biology Springer-Verlag, 1973 73(2010), 2 vom: 04. Sept., Seite 285-324 (DE-627)129391719 (DE-600)184905-0 (DE-576)014776863 0092-8240 nnns volume:73 year:2010 number:2 day:04 month:09 pages:285-324 https://doi.org/10.1007/s11538-010-9563-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 GBV_ILN_2010 GBV_ILN_2088 GBV_ILN_4012 GBV_ILN_4082 42.00 VZ AR 73 2010 2 04 09 285-324 |
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decomposition of neurological multivariate time series by state space modelling |
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Decomposition of Neurological Multivariate Time Series by State Space Modelling |
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Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. © Society for Mathematical Biology 2010 |
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Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. © Society for Mathematical Biology 2010 |
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Abstract Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison. © Society for Mathematical Biology 2010 |
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