中文摘要：

针对目前广泛使用的两种独立成分分析（ICA）算法（fixed—point算法和infomax算法）在处理功能磁共振成像（mRI）数据时速度较慢的特点，给出了独立成分分析的一个优化模型，在此基础上，提出了一种快速的牛顿型迭代算法．该算法采用修正后的牛顿迭代形式，使收敛速度达到三阶．将文中算法与其它两种算法应用于实际fMRI数据，实验结果表明，文中算法能够很好地分离出任务成分，同时大大减少了运算量，提高了运算速度，在处理大数据量的fMRI信号方面有明显的优势．

英文摘要：

As the fixed-point algorithm and the infomax algorithm, two of the most popular algorithms of indepen- dent component analysis （ICA）, spend too much time in processing functional magnetic resonance imaging （fMRI） data, an optimization model of ICA is presented. Based on the model, a fast Newton iteration algorithm is pro- posed, in which an improved Newton iteration method is adopted to achieve a three-order convergence speed. The proposed algorithm and the two above-mentioned algorithms are then used to process real fMRI data. The results show that the proposed algorithm well separates the independent components from fMRI data with less computation and high convergence speed, and that it has obvious advantages in processing fMRI signals with huge numbers of data.