Application of Kalman Filter to Remove TMS-Induced Artifacts from EEG Recordings

Abstract

Transcranial magnetic stimulation (TMS) is a technique in which a pulsed magnetic field created by a coil positioned next to the scalp is used to locally depolarize neurons in brain cortex. TMS can be combined with electroencephalography (EEG) to visualize regional brain activity in response to direct cortical stimulation, making it a promising tool for studying brain function. A technical drawback of EEG/TMS coregistrations is that the TMS impulse generates high amplitude and long-lasting artifacts that corrupt the EEG trace. In this brief, an offline Kalman filter approach to remove TMS-induced artifacts from EEG recordings is proposed. The Kalman filter is applied to the linear system arising from the combination of the dynamic models describing EEG and TMS signals generation. Time-varying covariance matrices suitably tuned on the physical parameters of the problem allow us to model the non-stationary components of the EEG/TMS signal, (neglected by conventional stationary filters). Experimental results show that the proposed approach guarantees an efficient deletion of TMS-induced artifacts while preserving the integrity of EEG signals around TMS impulses.