Stable EEG Source Estimation for Standardized Kalman Filter using Change Rate Tracking
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914211412574208 |
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| author | Lahtinen, Joonas |
| author_facet | Lahtinen, Joonas |
| contents | This article focuses on the measurement and evolution modeling of Standardized Kalman filtering for brain activity estimation using non-invasive electroencephalography data. Here, we propose new parameter tuning and a model that uses the rate of change in the brain activity distribution to improve the stability of otherwise accurate estimates. Namely, we propose a backward-differentiation-based measurement model for the change rate, which notably improves the filtering-parametrization-stability of the tracking. Simulated data and data from a real subject were used in experiments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_01984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable EEG Source Estimation for Standardized Kalman Filter using Change Rate Tracking Lahtinen, Joonas Applications Numerical Analysis Signal Processing 15A29, 60J22 G.3; I.6.5 This article focuses on the measurement and evolution modeling of Standardized Kalman filtering for brain activity estimation using non-invasive electroencephalography data. Here, we propose new parameter tuning and a model that uses the rate of change in the brain activity distribution to improve the stability of otherwise accurate estimates. Namely, we propose a backward-differentiation-based measurement model for the change rate, which notably improves the filtering-parametrization-stability of the tracking. Simulated data and data from a real subject were used in experiments. |
| title | Stable EEG Source Estimation for Standardized Kalman Filter using Change Rate Tracking |
| topic | Applications Numerical Analysis Signal Processing 15A29, 60J22 G.3; I.6.5 |
| url | https://arxiv.org/abs/2504.01984 |