Tracking EEG Thalamic and Cortical Focal Brain Activity using Standardized Kalman Filtering with Kinematics Modeling

TL;DR

This paper enhances EEG brain activity estimation by integrating kinematic models into Kalman filtering, improving depth accuracy and physical plausibility, especially for deep sources, with demonstrated effectiveness in simulations.

Contribution

It introduces first and second-order kinematic models into Kalman filtering for EEG, improving depth bias correction and estimate smoothness over prior methods.

Findings

Accurate tracking of superficial and deep brain activity demonstrated in simulations.

Kinematic models yield smoother, more plausible estimates under high noise conditions.

Enhanced computational efficiency through a tunable power parameter.

Abstract

Kalman filtering has proven to be effective for estimating brain activity using EEG recordings. In particular, the introduced post hoc standardization step of the algorithm, inspired by the sLORETA time-invariant method, reduces the depth bias and thus allows the estimation to appear at the correct depth from the electrode surface. In the current work, we propose first and second-order kinematic evolution models, where the state-space vector includes not only the dipolar source activity but also its velocity and acceleration. Compared to our previous study, this motion model yields smoother and more physically plausible estimates of brain activity even when the measurement noise is high, for both superficial and deep sources. In addition, we introduce a tunable power parameter that enhances the computational efficiency of the algorithm. Our simulation study, which involves thalamic and cortical activity in the somatosensory region, demonstrates that accurate estimation and tracking of both superficial and deep brain activity are feasible.