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  • Title: [A new method based on sparse component decomposition to remove MRI artifacts in the continuous EEG recordings].
    Author: Xu P, Chen H, Liu Z, Yao D.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2007 Apr; 24(2):439-43. PubMed ID: 17591277.
    Abstract:
    How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, when EEG and functional magnetic resonance imaging (FMRI) are simultaneous 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 waveletes and discrete cosine which can exhibit the temporal-spatial discrepancy between MRI artificats and EEG is constructed first, and then the signals are separated by learning in this MOD with matching pursuit (MP) algorithm. The method is applied to the MRI artifacts corrupted EEG recordings and the decomposition result shows its validation.
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