Schrödinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact

In EEG data acquired in the presence of fMRI, gradient-related spike artifacts contaminate signal following common preprocessing step average artifact subtraction. Spike compromise quality since they overlap with frequency, thereby confounding frequency-based inferences on activity. As well, can inflate or deflate correlations among time series, functional connectivity. We present Schrödinger filtering, which uses equation to decompose spike-containing input. The basis functions decomposition are localized and pulse-shaped, selectively capture various input peaks, components clustered at beginning spectrum. filtering automatically subtracts from data. On real simulated data, we show that (1) simultaneously accomplishes high removal preservation without affecting evoked activity, (2) reduces spurious pairwise spontaneous these regards, was significantly better than three other despiking techniques: median amplitude thresholding, wavelet denoising. These results encourage use future EEG-fMRI pipelines, as well spike-related applications (e.g., fMRI motion action potential extraction).