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Multiple sparse priors technique with optimized patches for brain source localization
International Journal of Imaging Systems and Technology ( IF 3.0 ) Pub Date : 2019-10-16 , DOI: 10.1002/ima.22370
Munsif Ali Jatoi 1 , Nidal Kamel 2 , José D. López 3
Affiliation  

Localizing brain neural activity using electroencephalography (EEG) neuroimaging technique is getting increasing response from neuroscience researchers and medical community. It is due to the fact that brain source localization has a variety of applications for diagnoses of various brain disorders. This problem is ill‐posed in nature because an infinite number of source configurations can produce the same potential at the head surface. Recently, a new technique that is based on Bayesian framework, called the multiple sparse priors (MSP), was proposed as a solution to this problem. The MSP develops the solution for source localization using the current densities associated with dipoles in terms of prior source covariance matrix and sensor covariance matrix, respectively. Then, it uses the maximization of the cost function of the free energy under the assumption of a fixed number of hyperparameters or patches in order to obtain the elements of prior source covariance matrix. This research work aims to further enhance the maximization process of MSP with regard to the free energy by considering a variable number of patches. This will lead to a better estimation of brain sources in terms of localization errors. The performance of the modified MSP with a variable number of patches is compared with the original MSP using simulated and real‐time EEG data. The results show a significant improvement in terms of localization errors.

中文翻译:

具有用于脑源定位的优化补丁的多重稀疏先验技术

使用脑电图 (EEG) 神经成像技术定位大脑神经活动正在得到神经科学研究人员和医学界越来越多的响应。这是因为脑源定位在诊断各种脑部疾病方面具有多种应用。这个问题本质上是不适定的,因为无数的源配置可以在头部表面产生相同的电位。最近,提出了一种基于贝叶斯框架的新技术,称为多重稀疏先验(MSP),作为该问题的解决方案。MSP 使用与偶极子相关的电流密度分别根据先验源协方差矩阵和传感器协方差矩阵开发源定位解决方案。然后,它在固定数量的超参数或补丁的假设下使用自由能的成本函数的最大化,以获得先验源协方差矩阵的元素。这项研究工作旨在通过考虑可变数量的补丁来进一步增强 MSP 在自由能方面的最大化过程。这将导致在定位错误方面更好地估计大脑来源。使用模拟和实时 EEG 数据将具有可变数量补丁的修改后 MSP 的性能与原始 MSP 进行比较。结果显示在定位错误方面有显着改善。这项研究工作旨在通过考虑可变数量的补丁来进一步增强 MSP 在自由能方面的最大化过程。这将导致在定位错误方面更好地估计大脑来源。使用模拟和实时 EEG 数据将具有可变数量补丁的修改后 MSP 的性能与原始 MSP 进行比较。结果显示在定位错误方面有显着改善。这项研究工作旨在通过考虑可变数量的补丁来进一步增强 MSP 在自由能方面的最大化过程。这将导致在定位错误方面更好地估计大脑来源。使用模拟和实时 EEG 数据将具有可变数量补丁的修改后 MSP 的性能与原始 MSP 进行比较。结果显示在定位错误方面有显着改善。
更新日期:2019-10-16
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