【摘要】 独立成分分析 (ICA)是统计信号处理中的一项新技术 ,用来从混合信号的多维观测中提取具有统计独立性的成分。我们针对功能磁共振数据处理 ,采用先对相邻的两体元信号作 ICA分离 ,然后与参考信号进行相关 ,把相关系数大于一定阈值的体元作为刺激引起兴奋的体元 ,从而实现刺激的功能定位。经实际脑功能磁共振数据试验 ,初步证明了方法的有效性更多还原

【Abstract】 Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.更多还原