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

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.