中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (26): 4801-4804.doi: 10.3969/j.issn.1673-8225.2010.26.012

• 数字化骨科 digital orthopedics • 上一篇    下一篇

基于皮质慢电位特征分析的神经皮质运动区功能定位

姜  涛，吴效明，叶丙刚   

  1. 华南理工大学，生物科学与工程学院，广东省广州市     510006
  • 出版日期:2010-06-25 发布日期:2010-06-25
  • 通讯作者: 吴效明，博士，博士生导师，华南理工大学，生物科学与工程学院，广东省广州市 510006 scutbme@scut. edu.cn
  • 作者简介:姜 涛，男，1965年生，辽宁省沈阳市人，汉族，华南理工大学在读博士，主要从事生物医学信息检测与处理。 taoojiang@ 163.com

Motor cortex function localization based on cortical slow potential analysis

Jiang Tao, Wu Xiao-ming, Ye Bing-gang   

  1. School of Bioscience & Bioengineering, South China University of Technology, Guangdong Province, Guangzhou  510006, China
  • Online:2010-06-25 Published:2010-06-25
  • Contact: Wu Xiao-ming, Doctor, Doctoral supervisor, School of Bioscience & Bioengineering, South China University of Technology, Guangdong Province, Guangzhou 510006, China scutbme@scut. edu.cn
  • About author:Jiang Tao Studying for doctorate, School of Bioscience & Bioengineering, South China University of Technology, Guangdong Province, Guangzhou 510006, China taoojiang@163.com

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448

被引次数

4

摘要：

背景：皮质慢电位及其偏移变化在所有个体中都存在，基于皮质慢电位及其变化规律检测的神经皮质(运动区)功能定位方法可以有效避免漏检，关于此方面的详细研究鲜有报道。
目的：探讨皮质脑电中皮质慢电位用于术中神经皮质(运动区)功能定位的可行性和特点。
方法：采集华盛顿西雅图海港医院3例患者位于大脑神经皮质(运动)功能区手指皮质区域的皮质脑电信号数据，同时采集相应手指弯曲运动数据，作自身对照。利用小波变换对皮质脑电信号进行分解和重构，提取运动事件相关皮质慢电位在运动事件发生前后的能量比(事件相关电位指标)为特征量，并构造特定阈值进行分类，结果与相应手指弯曲运动数据比较，进行检测正确率分析。将试验采集数据分成训练和测试组，分别用于特征提取和分类算法的设计和性能检测。
结果与结论：以皮质慢电位信号的事件相关电位指标为特征量，以1.6为阈值进行分类，其分类定位检出正确率达到84%。提示通过皮质(运动区)慢电位的特征提取和分类可以更有效地进行术中运动功能区皮质定位，具有检测分辨率高、避免漏检的优点。

关键词: 功能定位, 皮质慢电位, 事件相关电位, 小波分析, 特征提取

Abstract:

BACKGROUND: The cortical slow potential and its shift changes are present in all individuals. Based on slow cortical potentials and variation detection of neural cortex (motor area) brain mapping method can avoid the missed detection. Detailed studies on this aspect have been rarely reported.
OBJECTIVE: To investigate the characteristics of principle and feasibility of cortical electroencephalogram (EEG) slow cortical potentials for the intraoperative neural cortex (motor area) function.
METHODS: Brain cortex in the (motor) functional area of finger cortical areas of the cortex EEG data from 3 patients of Harbour Hospital was collected, and the corresponding finger bending motion data were collected as self-control. Wavelet decomposition and reconstruction of signals, extraction of sports event-related slow cortical potentials before and after the incident in the movement of energy (ERP indicators) as the characteristic parameter were performed, followed by construction of a particular threshold to classify. The outcome data were compared with the corresponding movement in bending finger, and the rate of correct detection was determined. The pilot data collected were divided into training and test groups, respectively for feature selection algorithms based classifier design and performance analysis.
RESULTS AND CONCLUSION: With a slow cortical potential target as the characteristic ERP signal volume and 1.6 as the threshold for classification, the correct detection rate of classification and positioning was 84%. Cortex (motor area) slow potential feature extraction and classification can be more effective for motor cortex localization with detection of high resolution, to avoid missed benefits.

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