Estimation of The Number of MEG Neural Activation Sources
脑磁图神经活动源数目的估计

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.