Noise covariance incorporated MEG-MUSIC algorithm: A method for multiple-dipole estimation tolerant of the influence of background brain activity
Noise covariance incorporated MEG-MUSIC algorithm: A method for multiple-dipole estimation tolerant of the influence of background brain activity
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DOI：
10.1109/10.623053
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发表时间：
1997-09-01
影响因子：
4.6
通讯作者：
Miyashita, Y
中科院分区：
文献类型：
--
作者：
Sekihara, K;Poeppel, D;Miyashita, Y
本文提出了一种从时空生物磁数据定位多个电流偶极子的方法。该方法基于多信号分类（MUSIC）算法，可以容忍背景大脑活动的影响。在此方法中，使用包含噪声但不包含任何信号信息的数据部分来估计噪声协方差矩阵。然后，使用噪声和测量数据协方差矩阵的广义特征向量形成改进的噪声子空间投影仪。使用该噪声子空间投影仪和噪声协方差矩阵来计算音乐定位器。计算机仿真结果验证了该方法的有效性。然后将该方法应用于音节语音引发的听觉诱发场的源估计。结果强烈表明该方法在消除背景活动影响方面的有效性。
This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The method is based on the multiple signal classification (MUSIC) algorithm and is tolerant of the influence of background brain activity. In this method, the noise covariance matrix is estimated using a portion of the data that contains noise, but does not contain any signal information. Then, a modified noise subspace projector is formed using the generalized eigenvectors of the noise and measured-data covariance matrices. The MUSIC localizer is calculated using this noise subspace projector and the noise covariance matrix. The results from a computer simulation have verified the effectiveness of the method. The method was then applied to source estimation for auditory-evoked fields elicited by syllable speech sounds. The results strongly suggest the method's effectiveness in removing the influence of background activity.