Danila Shelepenkov

The paper shows that alpha-band activity in V1 carries stimulus information with fine spatial specificity during figure-ground segregation, linking low-frequency rhythms to effective communication between cortical areas.

This study uses MEG and machine learning to decode spatial attention in humans during different tasks, showing dynamic, task-dependent attentional patterns similar to those in non-human primates.

This paper introduces a multilayer MEG source modelling framework to enable depth-resolved laminar inference in humans, showing it aligns with known neural circuit motifs.