Stable EEG source estimation for standardized Kalman filter using rate-of-change tracking

Localization of brain activity is an important guiding tool for presurgical evaluation and treatment planning, particularly in the context of various brain-related diseases such as epilepsy. Since brain activity is highly dynamic and arises from the firing of neuronal networks, advanced spatiotemporal modeling is needed to capture this time-varying behavior accurately.

A new parameter tuning and model utilizing the rate-of-change of brain activity distribution were developed to improve the filtering-parametrization-stability of the otherwise accurate estimation of the recently introduced Standardized Kalman filter. Namely, we propose a backward-differentiation-based measurement model for the rate of change. Simulated and real non-invasive electroencephalography data, along with realistic head models from two real subjects, were used in time-evolution tracking and localization experiments focusing on somatosensory evoked potentials. The method was compared to the original Standardized Kalman filter and Standardized Low-resolution Brain Electromagnetic Tomography (sLORETA).

Results indicate that the proposed parametrization yields high localization accuracy, as the original Standardized Kalman filtering localizes 7 and 6 out of 8, and sLORETA found 8 and 6 of the literature-defined originators of short latency somatosensory evoked potentials. The proposed standardized filtering method identified 7 of 8 expected originators. The change-rate-based model exhibits greater tracking stability than filtering without it against changes in filtering parameters. In addition, the method is more stable against badly set model parameters.

With new model parametrization, the studied standardized methodologies provide assumably accurate and stable estimations to explore the location and dynamical properties of cortical and subcortical brain activity. The results showing correct sub-thalamic localization demonstrate the significant potential of these methods in the guidance of stereo-electroencephalography sensor placements or the placement of implant electrodes for deep-brain stimulation.