一种基于稀疏分解去除EEG信号中MRI伪迹的新方法

A New Method Based on Sparse Component Decomposition to Remove MRI Artifacts in the Continuous EEG Recordings

在脑电图(Electroencephalography,EEG)和功能磁共振成像(Functional magnetic resonance imaging, FMRI)同时记录时,如何有效的去除混入EEG信号中的强磁共振(Magnetic resonance imaging,MRI)伪迹干扰信号是当前在EEG和FMRI的联合研究中面临的一个信号前期处理难点。主要从MRI干扰信号和EEG信号在时空上的差别出发,提出了一种基于混合过完备库的稀疏成分分析的分解方法,实现了强MRI干扰下的EEG信号的估计。在方法实现中,首先利用小波和离散余弦构造能体现MRI干扰和EEG时空特性差别的混合过完备库,然后通过匹配追踪(Matching pursuit,MP)方法在混合过完备库中的学习,实现MRI伪迹的消除。对模拟数据以及真实记录的混入了MRI干扰的EEG信号的估计实验结果,证实了该方法的有效性。

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.