Since functional Magnetic Resonance Imaging (fMRI) signals are a group of sparse signals, and its autocorrelation matrix contains limited information, it is difficult to accurately locate the brain activation area directly using traditional blind separation algorithms. For the issue, this paper proposes a method with inverse-sparse transform and second order blind identification (SOBI) for the separation of the activations. The contribution of this paper is to achieve the separation of sparse brain map signals and have lower computational complexity than higher-order statistical BSS. In experiments, we use both simulated and measured fMRI data to evaluate our method. The experimental results show that the proposed method's running time is only 1/30 of a higher-order statistical independent component analysis (ICA) algorithm, while its separation errors is close to ICA and less than half of a traditional SOBI algorithm.

由于功能性磁共振成像（fMRI）信号是一组稀疏信号，其自相关矩阵包含的信息有限，传统的盲分离算法难以直接准确定位大脑激活区域。针对这个问题，本文提出了一种具有逆稀疏变换和二阶盲识别（SOBI）的方法来分离激活。本文的贡献是实现了稀疏脑图信号的分离，并且比高阶统计BSS具有更低的计算复杂度。在实验中，我们使用模拟和测量的 fMRI 数据来评估我们的方法。实验结果表明，该方法的运行时间仅为高阶统计独立分量分析（ICA）算法的1/30，