独立成分分析（independent component analysis, ICA）采用一种统计隐变量模型，假设信号是由各信源线性叠加构成。为了解决功能磁共振数据（functional magnetic resonance imaging, fMRI）中由于信源非线性叠加造成的ICA检测误差，提出了基于瞬时功率的ICA方法。首先，由电流能量形式将fMRI数据推广为fMRI能量信号；然后，由血氧水平依赖（blood oxygenation level dependent, BOLD）信号与T*2信号的关系，给出了两种反映BOLD能量变化的瞬时功率fMRI信号；最后，采用空间ICA分析fMRI瞬时功率信号，得到与各脑部活跃区域能量相关的独立成分。从理论和仿真试验两个方面阐明了新方法的合理性和优越性，同时应用于实际癫痫fMRI数据，经与传统ICA方法比较，该方法能够在静息态下鲁棒地检测脑部能量异常区域。

In independent component analysis (ICA), a statistical latent variables model is employed to assume that the obtained data is a linear mixture of signals. To deal with the detection error when the nonlinear mixing signals of functional magnetic resonance imaging (fMRI) data are decomposed by means of ICA, a novel ICA method based on the instantaneous power of fMRI data is developed. Firstly, fMRI data are converted into its energy signals according to the energy form of electricity. Secondly, according to the relationship between blood oxygenation level dependent (BOLD) and T*2 signal, two types of instantaneous power of fMRI signals which represent the energy fluctuations of BOLD are proposed. Finally, based on the instantaneous power of fMRI data, the components correlated with the energies of brain activations are obtained by using a spatial ICA method. The effectiveness and advantage are elucidated through theoretical analyses and simulation tests, and it is also applied to vivo experimental epileptic fMRI, the results show that our method can robustly detect abnormal brain activities at resting state compared with the traditional ICA methods.