Jafadideh Alireza Talesh, Asl Babak Mohammadzadeh
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Brain Topogr. 2018 Jul;31(4):591-607. doi: 10.1007/s10548-018-0645-8. Epub 2018 Apr 27.
Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.
基于自适应最小方差的波束形成器（MVB）已成功应用于脑磁图（MEG）和脑电图（EEG）数据，以定位大脑活动。然而，在存在相关或干扰源的情况下，这些波束形成器的性能会下降。为了克服这个问题，我们提出在脑源定位中应用间接主导模式抑制（iDMR）波束形成器。该方法通过修改测量协方差矩阵，使MVB适用于存在相关和干扰源的源定位。对EEG和MEG数据的数值结果表明，所提出的方法能够准确地重建活动源的时间进程，并以高空间分辨率定位这些源。此外，实际听觉诱发场（AEF）数据的结果表明iDMR在实际情况下具有良好的性能。因此，iDMR可以可靠地用于脑源定位，特别是在存在相关和干扰源的情况下。
