Considerations on Strategies to Improve EOG Signal Analysis
Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the...
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| Autor*in: |
Palaniappan, Ramaswamy [verfasserIn] Wissel, Tobias [author] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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Online-Ressource |
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IGI Global InfoSci Journals Archive 2000 - 2012 |
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In: International journal of artificial life research - Hershey, Pa : IGI Global, 2010, 2(2011), 3, Seite 6-21 |
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volume:2 ; year:2011 ; number:3 ; pages:6-21 |
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10.4018/jalr.2011070102 |
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| 520 | |a Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions | ||
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10.4018/jalr.2011070102 doi (DE-627)NLEJ24444496X (VZGNL)10.4018/jalr.2011070102 DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut Considerations on Strategies to Improve EOG Signal Analysis 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions IGI Global InfoSci Journals Archive 2000 - 2012 Autoregressive Electrooculogram Human-Computer Interfaces Virtual Keyboard Wavelet Decomposition Wissel, Tobias author aut In International journal of artificial life research Hershey, Pa : IGI Global, 2010 2(2011), 3, Seite 6-21 Online-Ressource (DE-627)NLEJ244418608 (DE-600)2696257-3 1947-3079 nnns volume:2 year:2011 number:3 pages:6-21 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 3 6-21 |
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10.4018/jalr.2011070102 doi (DE-627)NLEJ24444496X (VZGNL)10.4018/jalr.2011070102 DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut Considerations on Strategies to Improve EOG Signal Analysis 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions IGI Global InfoSci Journals Archive 2000 - 2012 Autoregressive Electrooculogram Human-Computer Interfaces Virtual Keyboard Wavelet Decomposition Wissel, Tobias author aut In International journal of artificial life research Hershey, Pa : IGI Global, 2010 2(2011), 3, Seite 6-21 Online-Ressource (DE-627)NLEJ244418608 (DE-600)2696257-3 1947-3079 nnns volume:2 year:2011 number:3 pages:6-21 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 3 6-21 |
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10.4018/jalr.2011070102 doi (DE-627)NLEJ24444496X (VZGNL)10.4018/jalr.2011070102 DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut Considerations on Strategies to Improve EOG Signal Analysis 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions IGI Global InfoSci Journals Archive 2000 - 2012 Autoregressive Electrooculogram Human-Computer Interfaces Virtual Keyboard Wavelet Decomposition Wissel, Tobias author aut In International journal of artificial life research Hershey, Pa : IGI Global, 2010 2(2011), 3, Seite 6-21 Online-Ressource (DE-627)NLEJ244418608 (DE-600)2696257-3 1947-3079 nnns volume:2 year:2011 number:3 pages:6-21 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 3 6-21 |
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10.4018/jalr.2011070102 doi (DE-627)NLEJ24444496X (VZGNL)10.4018/jalr.2011070102 DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut Considerations on Strategies to Improve EOG Signal Analysis 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions IGI Global InfoSci Journals Archive 2000 - 2012 Autoregressive Electrooculogram Human-Computer Interfaces Virtual Keyboard Wavelet Decomposition Wissel, Tobias author aut In International journal of artificial life research Hershey, Pa : IGI Global, 2010 2(2011), 3, Seite 6-21 Online-Ressource (DE-627)NLEJ244418608 (DE-600)2696257-3 1947-3079 nnns volume:2 year:2011 number:3 pages:6-21 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jalr.2011070102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2011 3 6-21 |
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Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions |
| abstractGer |
Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions |
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Electrooculogram (EOG) signals have been used in designing Human-Computer Interfaces, though not as popularly as electroencephalogram (EEG) or electromyogram (EMG) signals. This paper explores several strategies for improving the analysis of EOG signals. This article explores its utilization for the extraction of features from EOG signals compared with parametric, frequency-based approach using an autoregressive (AR) model as well as template matching as a time based method. The results indicate that parametric AR modeling using the Burg method, which does not retain the phase information, gives poor class separation. Conversely, the projection on the approximation space of the fourth level of Haar wavelet decomposition yields feature sets that enhance the class separation. Furthermore, for this method the number of dimensions in the feature space is much reduced as compared to template matching, which makes it much more efficient in terms of computation. This paper also reports on an example application utilizing wavelet decomposition and the Linear Discriminant Analysis (LDA) for classification, which was implemented and evaluated successfully. In this application, a virtual keyboard acts as the front-end for user interactions |
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Considerations on Strategies to Improve EOG Signal Analysis |
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