A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography
Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals...
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/540 |
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| author | Palpolage Don Shehan Hiroshan Gunawardane Raymond Robert MacNeil Leo Zhao James Theodore Enns Clarence Wilfred de Silva Mu Chiao |
| author_facet | Palpolage Don Shehan Hiroshan Gunawardane Raymond Robert MacNeil Leo Zhao James Theodore Enns Clarence Wilfred de Silva Mu Chiao |
| author_sort | Palpolage Don Shehan Hiroshan Gunawardane |
| collection | DOAJ |
| description | Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods. |
| first_indexed | 2024-03-08T09:46:20Z |
| format | Article |
| id | doaj.art-2541f130be8c4007a6c0cd1dfb773816 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:20Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2541f130be8c4007a6c0cd1dfb7738162024-01-29T14:16:07ZengMDPI AGSensors1424-82202024-01-0124254010.3390/s24020540A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with ElectrooculographyPalpolage Don Shehan Hiroshan Gunawardane0Raymond Robert MacNeil1Leo Zhao2James Theodore Enns3Clarence Wilfred de Silva4Mu Chiao5Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Psychology, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Psychology, The University of British Columbia, Vancouver, BC V6T 1Z4, CanadaElectrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.https://www.mdpi.com/1424-8220/24/2/540biomedical signal processingcorneo-retinal potentialelectrooculographyfiltering algorithmseye trackingKalman filters |
| spellingShingle | Palpolage Don Shehan Hiroshan Gunawardane Raymond Robert MacNeil Leo Zhao James Theodore Enns Clarence Wilfred de Silva Mu Chiao A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography Sensors biomedical signal processing corneo-retinal potential electrooculography filtering algorithms eye tracking Kalman filters |
| title | A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography |
| title_full | A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography |
| title_fullStr | A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography |
| title_full_unstemmed | A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography |
| title_short | A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography |
| title_sort | fusion algorithm based on a constant velocity model for improving the measurement of saccade parameters with electrooculography |
| topic | biomedical signal processing corneo-retinal potential electrooculography filtering algorithms eye tracking Kalman filters |
| url | https://www.mdpi.com/1424-8220/24/2/540 |
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