论文标题
EEG源本地化的稀疏算法
Sparse algorithms for EEG source localization
论文作者
论文摘要
使用脑电图的来源定位对于诊断与大脑相关的各种生理和精神疾病很重要。脑电图的高度时间分辨率有助于医学专业人员以更有信息的方式评估大脑的内部生理学。内部来源是通过反转过程从脑电图中获得的。大脑中的来源数量超过了测量的数量。在本文中，介绍了该领域中最稀疏的来源本地化方法的全面综述。最近开发的方法是基于确定性的减少稀疏解决方案（CARSS），并进行了检查。使用涉及两个源空间的六十四个通道设置进行大量比较研究。第一个源空间有5004个来源，另一个源空间有2004年的来源。考虑了四个，三个，三个，五个和七个模拟主动源的测试用例。无噪声数据也添加了两个噪声水平。还评估了汽车。检查结果。还尝试了一项真正的脑电图研究。
Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state of the art sparse source localization methods in this field is presented. A recently developed method, certainty based reduced sparse solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty four channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted.
