时频脑磁图-多重信号分类算法

Time-frequency MEG-MUSIC algorithm.

作者信息

Sekihara K, Nagarajan S, Poeppel D, Miyashita Y

机构信息

Mind Articulation Project, Japan Science and Technology Corporation, Bunkyo, Tokyo.

出版信息

IEEE Trans Med Imaging. 1999 Jan;18(1):92-7. doi: 10.1109/42.750262.

DOI:10.1109/42.750262

PMID:10193700

Abstract

We propose a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. The method is based on the multiple-signal-classification (MUSIC) algorithm and it calculates a time--frequency matrix in which diagonal and off-diagonal terms are the auto and crosstime--frequency distributions of multichannel MEG recordings, respectively. The method averages this time-frequency matrix over the time--frequency region of interest. The locations of neural sources are then estimated by checking the orthogonality between the noise subspace of this averaged matrix and the sensor lead field. Accordingly, the method allows us to estimate the locations of neural sources from each time--frequency component. A computer simulation was performed to test the validity of the proposed method, and the results demonstrate its effectiveness.

摘要

我们提出了一种将神经源的时频特征纳入脑磁图（MEG）源估计的方法。该方法基于多重信号分类（MUSIC）算法，它计算一个时频矩阵，其中对角项和非对角项分别是多通道MEG记录的自时频分布和互时频分布。该方法在感兴趣的时频区域上对这个时频矩阵求平均。然后，通过检查这个平均矩阵的噪声子空间与传感器导联场之间的正交性来估计神经源的位置。因此，该方法使我们能够从每个时频分量估计神经源的位置。进行了计算机模拟以测试所提出方法的有效性，结果证明了其有效性。