Advanced algorithms for identification of electrophysiological features underlying encoding and recall of human memory in intracranial EEG

Deficits in memory and cognition are one of the main health problems of our aging society with very few therapeutic options. Direct brain stimulation in specific brain regions has recently emerged as a promising tool to enhance performance in memory tasks. In this project, we aim to identify neurophysiological events that are related to memory encoding and recall. To achieve this goal we will use data from cognitive tasks performed by epileptic patients and advanced signal processing algorithms. The used algorithms are focused on the processing of intracranial EEG to detect high frequency events and to compute functional brain connectivity. Apart from intracranial EEG we will also analyze exact times of eye movements in response to presentation and vocalization of specific words. The ultimate aim of this project will be the creation of a machine learning model for prediction of memory formation. The results of this project will broaden our knowledge of memory encoding and recall and will improve therapeutic approaches to treat memory with deep brain stimulation.