Stable EEG Source Estimation for Standardized Kalman Filter using Change Rate Tracking

Abstract

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.