A new method based on sparse component decomposition to remove MRI artifacts in the continuous EEG recordings.
- Author:
Peng XU
1
;
Huafu CHEN
;
Zuxiang LIU
;
Dezhong YAO
Author Information
1. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artifacts;
Electroencephalography;
Evoked Potentials;
Magnetic Resonance Imaging;
Phantoms, Imaging;
Principal Component Analysis;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2007;24(2):439-443
- CountryChina
- Language:Chinese
-
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.
