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.