Sekihara K, Nagarajan S S, Poeppel D, Miyauchi S, Fujimaki N, Koizumi H, Miyashita Y
Japan Science and Technology Corporation, Tokyo, Japan.
IEEE Trans Biomed Eng. 2000 May;47(5):642-53. doi: 10.1109/10.841336.
We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time-frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.
我们开发了一种将神经源的时频特性纳入脑磁图（MEG）源估计的方法。这种方法被称为时频多重信号分类算法，它允许从任何感兴趣的时频区域估计神经源的位置。在本文中，我们基于二次时频表示的最一般形式来阐述该方法。然后，我们将其应用于两种非平稳MEG数据：伽马波段（频率范围在30 - 100赫兹之间）听觉活动数据和自发MEG数据。我们的方法成功检测到了位于N1m源位置稍内侧的伽马波段源。该方法能够选择性地定位不同位置的阿尔法节律爆发源。它还从受试者闭眼测量的以阿尔法节律为主的MEG数据中检测到了缪节律源。这些应用结果验证了时频MUSIC算法在选择性定位具有不同时频特征源方面的有效性。
