Morbidi Fabio, Garulli Andrea, Prattichizzo Domenico, Rizzo Cristiano, Manganotti Paolo, Rossi Simone
Department of Information Engineering, University of Siena, Via Roma 56, 53100 Siena, Italy.
J Neurosci Methods. 2007 May 15;162(1-2):293-302. doi: 10.1016/j.jneumeth.2006.12.013. Epub 2007 Jan 7.
In this paper we present an off-line Kalman filter approach to remove transcranial magnetic stimulation (TMS)-induced artifacts from electroencephalographic (EEG) recordings. Two dynamic models describing EEG and TMS signals generation are identified from data and the Kalman filter is applied to the linear system arising from their combination. The keystone of the approach is the use of time-varying covariance matrices suitably tuned on the physical parameters of the problem that allow to model the nonstationary components of the EEG-TMS signal. This guarantees an efficient deletion of TMS-induced artifacts while preserving the integrity of EEG signals around TMS impulses. Experimental results show that the Kalman filter is more effective than stationary filters (Wiener filter) for the problem under investigation.
在本文中，我们提出了一种离线卡尔曼滤波器方法，用于从脑电图（EEG）记录中去除经颅磁刺激（TMS）诱发的伪迹。从数据中识别出两个描述EEG和TMS信号生成的动态模型，并将卡尔曼滤波器应用于由它们组合产生的线性系统。该方法的关键在于使用时变协方差矩阵，该矩阵根据问题的物理参数进行适当调整，从而能够对EEG-TMS信号的非平稳成分进行建模。这确保了在保留TMS脉冲周围EEG信号完整性的同时，有效地消除TMS诱发的伪迹。实验结果表明，对于所研究的问题，卡尔曼滤波器比平稳滤波器（维纳滤波器）更有效。
