Estimation of Baseline Drifts in fMRI

This work provides a new method to estimate and remove baseline drifts in the fMRI signal. The baseline drift in each time series is described as a superposition of physical and physiological phenomena that occur at different scales. A fast algorithm, based on a wavelet representation of the data yields detrended time-series. Experiments with fMRI data demonstrate that our detrending technique can infer and remove drifts that cannot be adequately represented with low degree polynomials. Our detrending technique resulted in a noticeable improvement by reducing the number of false positive and the number of false negative.