A model-free de-drifting approach for detecting BOLD activities in fMRI data
A model-free method for efficiently capturing drifts in functional magnetic resonance imaging (fMRI) data is presented. The proposed algorithm applies a first order differencing to the fMRI time series samples in order to remove the drift effect. Initially, a consistent hemodynamic response function (HRF) of the fMRI voxel is estimated using linear least-squares. An optimal estimate of the drift is then obtained based on a wavelet thresholding technique applied to the generated...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Signal Processing Systems|
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