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J Neurosci Methods. 2011 May 15;198(1):135-46. doi: 10.1016/j.jneumeth.2011.03.022. Epub 2011 Apr 2.
Over the last few years, deep brain stimulation (DBS) with targets such as the subthalamic nucleus or the pallidum were found to be beneficial in the treatment of Parkinson's disease and dystonia. The investigation of the mechanisms of action of DBS by recording concomitant neural activities in basal ganglia is hampered by the large stimulus artefacts (SA). Approaches to remove the SA with conventional filters, or other conventional digital methods, are not always effective due to the significant overlap between the spectral contents of the neuronal signal and the SA. Thus, such approaches may produce a significant residual SA or alter the neuronal signal dynamics by removing its frequency contents. In this work, we propose a method based on an on-line SA template extraction and on the Ensemble empirical mode decomposition (EEMD) to automatically detect and remove the dynamics of the SA without altering the embedded dynamics of the neuronal signal during stimulation. The results, based on real signals recorded in the subthalamic nucleus during Motor cortex stimulation (MCS) experiments, show that this technique, which may be applied on-line, effectively identifies, separates and removes the SA, and uncovers neuronal potentials superimposed on the artefact.
在过去的几年中，已经发现针对丘脑底核或苍白球等靶点的深部脑刺激（DBS）对治疗帕金森病和肌张力障碍有益。通过记录基底神经节中伴随的神经活动来研究 DBS 的作用机制，由于神经元信号和刺激伪迹（SA）的频谱内容之间存在显著重叠，因此受到大型刺激伪迹（SA）的阻碍。使用传统滤波器或其他传统数字方法来去除 SA 的方法并不总是有效，因为这可能会产生明显的残留 SA 或通过去除其频率内容来改变神经元信号的动态。在这项工作中，我们提出了一种基于在线 SA 模板提取和集合经验模态分解（EEMD）的方法，可在不改变刺激期间神经元信号嵌入动态的情况下自动检测和去除 SA 动态。基于在运动皮层刺激（MCS）实验期间记录的丘脑底核中的真实信号的结果表明，该技术可以在线应用，有效地识别、分离和去除 SA，并揭示叠加在伪迹上的神经元电位。
