Electrical neural activity monitoring and recording will increase our understanding of how the human brain works. Tracking mechanisms of neural activity led to better diagnosis and management of severe neurological conditions such as Parkinson’s disease and epilepsy. More importantly, these approaches were used to distinguish between various types of seizures based on the location and direction of the seizure foci, thereby increasing the outcomes of epilepsy surgery. A detailed study was carried out on the role of neural synchrony in brain functions with Electroencephalography (EEG). Most studies had been conducted on EEG connectivity analysis at sensor level. It is not easy to evaluate the connected networks because the volume conductive effect significantly distorts signals because of the electrical conductiveness of the head and often scalp electrodes derive input from the same sources in the brain. These factors help to estimate the real connectivity between brain regions inaccurately. The suggested approach is referred to as EEG source connectivity. The inverse problem is the estimation of the localized current dipole model from the EEG measurements. In order to solve the inverse EEG problem, advanced signal-processing algorithms such as the efficient implementation of PF have been built to facilitate direct exposure to neural dipole sources in real time and measure the connectivity of neural sources time courses using functional and effective connectivity measures.

中文翻译：

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使用有效的连通性措施和粒子过滤器评估神经源之间的方向连通性

电神经活动监测和记录将增加我们对人脑如何工作的理解。神经活动的跟踪机制可以更好地诊断和管理严重的神经系统疾病，例如帕金森病和癫痫症。更重要的是，这些方法用于根据癫痫病灶的位置和方向区分各种类型的癫痫发作，从而提高癫痫手术的结果。用脑电图 (EEG) 对神经同步在脑功能中的作用进行了详细研究。大多数研究都是在传感器水平上对脑电图连通性分析进行的。评估连接的网络并不容易，因为体积传导效应会因为头部的导电性而显着扭曲信号，并且头皮电极通常从大脑中的相同来源获得输入。这些因素有助于不准确地估计大脑区域之间的真实连通性。建议的方法称为 EEG 源连接。逆问题是从 EEG 测量中估计局部电流偶极子模型。为了解决逆 EEG 问题，已经建立了先进的信号处理算法，例如 PF 的有效实现，以促进实时直接暴露于神经偶极子源，并使用功能性和有效的连通性测量来测量神经源时间过程的连通性.