A new method based on sparse component decomposition to remove MRI artifacts in the continuous EEG recordings

How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, induced when EEG and functional magnetic resonance imaging (FMRI) are simultaneously recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the mixed over-complete dictionary is proposed in this paper to remove MR artifacts. A mixed over-complete dictionary(MOD) of wavelet and discrete cosine which can exhibit the temporal-spatial discrepancy between MRI artifacts and EEG is constructed first, and then the signals are separated by learning in this MOD with matching pursuit(MP) algorithm. After the sparse decomposition in MOD, the filtered EEG is approximately represented by the linear combination of atoms in the wavelet overcomplete dictionary and the removed MRI artifacts by that in the discrete cosine dictionary. The method is applied to the MRI artifacts corrupted EEG recordings and the decomposition result shows its validation