Error Reduction in Dipole-Source Localization Models

描述（由申请人提供）： 我们正在开发减少偶极子源定位（DSL）错误的方法。DSL通常用于分析脑电波，用于临床和研究目的。 从我们的工作和其他人的工作中可以知道，用于计算DSL的模型并不完美，但不知道不完美的模型对所获得的解决方案有多大影响。本文在前人工作的基础上，引入一些新的概念，提出了偶极源模型误差（DSME）。我们已经在球体中测试了它的功效，并将在本项目的头型模型中进行测试。我们将通过模拟来比较误差，并通过确定DSL在分析来自已知神经发生器的诱发响应时是否存在误差来比较误差。 与健康有关的方面包括诱发反应和DSL的临床应用，以及技术准确性的提高将改善这些应用的事实。

DESCRIPTION (provided by applicant): We are developing means to reduce errors in Dipole Source Localization (DSL). DSL is commonly used to analyze brain waves for clinical and research purposes. It is known from our work and the work of others that the models used to compute the DSL are not perfect, but it is not known how much the imperfect models affect the solutions obtained. Based upon our prior work, and some new concepts, we have developed the DSME (Dipole-Source Model- Error). We have tested its efficacy in spheres, and will do so in headshape models in this project. We will compare errors both by simulations, and also by determining whether the DSL has errors when analyzing evoked-responses from known neural generators. The health-related aspects include the clinical uses of evoked-responses and DSL, and the fact that improvements in the accuracy of the technique will improve these uses.