基于非负性约束K-SVD的fMRI盲源信号分离

摘要

近年来,K-SVD算法在功能磁共振成像(functional magnetic resonance imaging,fMRI)数据分析方法的研究中越来越受到关注.在本文中,提出了一种新的基于非负性约束K-SVD(Non-negative K-SVD,NK-SVD)的盲源信号分离(Blind Source Separation,BSS)方法.首先,随机初始化字典矩阵,利用正交匹配追踪算法(Orthogonal Matching Pursuit,OMP)求得稀疏向量矩阵;然后利用NK-SVD迭代更新字典矩阵和稀疏向量矩阵;进一步,对字典矩阵求伪逆,乘以原始信号数据,可得到脑功能激活区;最后,将本文的方法应用于模拟数据和真实数据,结果证明了方法的有效性,并且比传统算法有更好的效果.%In recent years,the K-SVD algorithm has gained more and more attention in the studies of functional magnetic resonance imaging (fMRI) data analysis.In this research,we propose a new method of blind source separation based on non-negative constrained K-SVD (NK-SVD).Firstly,we initialize a dictionary matrix randomly,and use orthogonal matching pursuit (OMP) to obtain a sparse vector matrix.Then,we use NK-SVD to update the dictionary matrix and sparse vector matrix.Furthermore,we solve the dictionary matrix pseudo inverse to obtain the brain functional activation areas by multiplying by the original data.Finally,we apply the proposed method to both simulated data and real fMRI data,where the correspondingly experimental results demonstrate the effectiveness of the proposed one,having better performance in comparison with the conventional algorithms.