This article focuses on the measurement and evolution modeling of Standardized Kalman filtering in brain activity estimation when non-invasive electroencephalography measurements are used as the data. Here, we propose new parameter tuning and model utilizing the change rate of brain activity distribution to improve the stability of the otherwise accurate estimation. Namely, we pose a backward differentiation-based measurement model for the change rate that increased the stability of the tracking notably. Simulated data and data from a real subject were used in experiments.

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