脑磁图神经活动源数目的估计

在脑磁图信号的分析中，正确估计出脑磁图神经活动源的数目是进一步分析脑磁图信号的前提。目前广泛采用的信息论方法和主成分分析方法都是根据特征值来确定源的数目，这两种方法在源数目较多、噪声较强的情况下，会导致误判。该文提出了一种噪声调节自动阈值的脑磁图源数目判断方法，利用基于噪声调节的主成分分析并结合聂曼- 皮尔逊准则对脑磁图源数目进行估计。同时，该方法采用了基于小波的噪声方差估计，实现了脑磁图信号中噪声方差的精确估计。通过对基于信息论方法、主成分分析方法以及该文所提议方法的实验结果的比较，表明该文所提议方法能更准确地估计脑磁图源数目，特别是在源数目较多、信噪比较小的情况下，仍能准确地估计脑磁图源数目，具有较大的实际意义。

Estimation of The Number of MEG Neural Activation Sources

It is very crucial to estimate the number of neural activation sources in the magnetoencephalographic data analysis. In the present study, information criterion method and principle component analysis have been applied to detect the number of the sources. These methods are both based on the eigenvalue analysis, and they are easily affected by noise. Accordingly, a new method, called noise-adjusted automatic threshold method, is proposed here to solve this problem. The method is based on the noise-adjusted principal component analysis. Furthermore, combined with the Neyman-Pearson criteria and a wavelet-based noise variance estimation method, the proposed method could successfully reduce the effect of noise on the estimation of number of the neural activation sources. The computer simulation results showed that the proposed method could provide an effective means for estimation of number of the MEG neural activation sources.