The Development of New Tensor Decomposition Algorithms to Reveal the Connectivity of Cognitive Learning using EEG

学习是所有人的一项基本技能，我们一生中有很大一部分时间都在学习。但有些人比另一些人更好，一些人获得技能的速度更快，他们与其他人的不同之处可能是他们的神经网络的结构方式。学习需要时间，并有不同的阶段，发生在大脑的不同部分。为了更好地理解学习的动态功能连通性，一种潜在的方法是将张量分解算法应用于在学习过程中记录的脑电数据。这将把大脑活动分解成其功能成分，通过两个或多个成分之间的耦合可以找到这些功能成分的连通性。本课题旨在开发新的张量分解算法，为认知学习过程中神经网络的连通性拓扑结构带来新的见解。

Learning is a fundamental skill to all people, we spent a big portion of our lives learning. But some are better than others, some acquire skills faster and what makes them different from the rest is likely the way their neural networks are structured.Learning takes time and has various stages which occur in various parts of the brain. One potential method to get a better understanding about the dynamic functional connectivity of learning is by applying tensor decomposition algorithms to EEG data recorded during the learning process. This will decompose the brain activity into it's functional components, for which connectivity can be found using coupling between 2 or more components.This topic aims to develop new tensor decomposition algorithms and bring new insight into the connectivity topology of neural networks during cognitive learning.