TMS artifact removal from neurophysiolical recordings using a novel iterative adaptive filtering
A novel iterative adaptive filtering approach is proposed to remove the Transcranial Magnetic Stimulation (TMS) induced artifact from multi-channel recordings of neural responses to sensory stimuli. For each specific channel, the average of all trials is considered as the input to the adaptive filter whose coefficients are calculated by minimizing the mean square error between the voltage trace of that trial and the filter output. The residues of all trials serve as an initial estimate of the neural response. Once this estimate is calculated, the input of the adaptive filter is modified by subtracting the mean residue. It is shown that the modified input provides a better estimate of the mean TMS artifact, which serves the input of the adaptive filer, in the next iteration. Therefore, new filter coefficients are estimated in the next iteration, for each single trial, and the procedure continues till no considerable changes in the residues occur. We report a quantitative verification of the accuracy of our method by generating a controlled simulation. Furthermore, applying the algorithm to experimental data confirms the accuracy of our approach and its usefulness for extracting neurophysiological responses occurring in temporal proximity to TMS pulses.