My research focuses on the role of gamma oscillations during visual information processing and the neuronal and cognitive underpinnings of spatial cognition.  We use EEG, ERP, ECoG, EEG, and intracranial single-unit data. In addition, I pursue theoretical studies in computational neuroscience.

The goal is to develop machine learning algorythms that allows for prediction of discovies in the near future based on large data bases of scientific literature.

This research focuses on how humans acquire the concept of time, its ontogenesis, and its transformation over a lifetime. Because the subjective experience of time is subject to distortions, we address the cognitive mechanisms underlying these distortions.

This project aims to extract signals from EEG related to information processing in the brain. The brain processes information in chunks of epochs deeply hidden in the raw EEG signal. We develop methods to localize those intervals and extract the signal components related to information update in the brain when new information is being processed and integrated without averaging across many trials. 

We study the theory of neural coding, more specifically, the principle of phase coding, the empirical and theoretical aspects and its computational efficacy.