Bahador Nooshin, Saha Josh, Rezaei Mohammad R, Utpal Saha, Ghahremani Ayda, Chen Robert, Lankarany Milad
Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada.
Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada.
Bioengineering (Basel). 2023 Jun 14;10(6):719. doi: 10.3390/bioengineering10060719.
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.
深部脑刺激（DBS）作为运动障碍患者的一种治疗选择被广泛应用。除了其临床影响外，DBS还被用于认知神经科学领域，在该领域中，关于决策中神经调节机制的几个基本问题的答案依赖于以临床频率（即130Hz）通常传递的一阵DBS脉冲干扰参与者选择的方式。据观察，在DBS期间记录的神经活动被持续几毫秒的大伪迹以及可持续数百毫秒的低频（慢）信号（~1-2Hz）所污染。虽然大多数去除DBS伪迹的方法重点关注前者，但慢信号的伪迹去除能力尚未得到解决。在这项工作中，我们提出了一种基于奇异值分解（SVD）和归一化自适应滤波相结合的新方法，以去除在利用DBS脉冲的认知任务期间记录的局部场电位中的大（快）和慢伪迹。使用合成数据，我们表明我们提出的算法优于文献中四种常用技术，即（1）归一化最小均方自适应滤波，（2）最优FIR维纳滤波，（3）高斯模型匹配，以及（4）移动平均。该算法在言语Stroop任务期间从丘脑底核记录的局部场电位中有效去除DBS伪迹的能力进一步证明了其能力，突出了其在实际应用中的实用性。
