Depth-Time Interpolation of Feature Trends Extracted from Mobile Microelectrode Data with Kernel Functions

机译：利用核函数从移动微电极数据中提取特征趋势的深度时间插值

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Background/AimsMicroelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery.

机译：背景/目的在深部脑刺激（DBS）手术期间，微电极记录（MER）对于精确定位目标结构（例如丘脑下核）是必需的。尝试使此过程自动化已产生了从移动MER数据中提取的定量时间趋势（功能活动与时间）。我们的目标是评估从时间趋势生成空间轮廓（特征活动与深度）的计算方法，这些方法将自动MER定位与临床程序脱钩，并增强DBS手术中的功能定位。

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期刊名称 Stereotactic and Functional Neurosurgery
作者
Stephen Wong; Eric L. Hargreaves; Gordon H. Baltuch; Jurg L. Jaggi; Shabbar F. Danish;
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年(卷),期 -1(90),1
年度 -1
页码 51–58
总页数 8
原文格式 PDF
正文语种
中图分类神经科学;外科学;
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1. Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions [J] . WongS., HargreavesE.L., BaltuchG.H., Stereotactic and Functional Neurosurgery: Official Journal of the World Society for Stereotactic and Functional Neurosurgery . 2012,第1期

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Depth-Time Interpolation of Feature Trends Extracted from Mobile Microelectrode Data with Kernel Functions

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