Wim van Drongelen

This paper develops a real-time sleep stage classifier for N2 and N3 sleep using scalp and intracranial EEG, showing high accuracy and informing stimulation timing.

This study shows thalamic stereo-EEG can detect sleep stages N2 and N3 with high accuracy, comparable to scalp EEG, using machine learning classifiers.

The paper identifies theta-band power as a predictor of slow oscillation (SO) initiation and spatial extent using EEG data from healthy adults.

The paper examines how different thalamic nuclei coordinate with cortical slow oscillations during sleep, finding nucleus-specific patterns linked to propagation types.

The paper investigates how slow oscillations gate interictal spikes across the thalamocortical-epileptogenic network in human epilepsy, finding region-specific coupling and predictive pre-onset phase-amplitude coupling.

This paper introduces 4D Shannon's entropy as a novel metric for characterizing neural network activity using triple correlation, showing it reveals more network organization than pairwise entropy.

This study develops an automated synapse quantification algorithm to analyze synaptogenesis in dissociated hippocampal cultures and finds increased synaptic density with maturation and a reduction in excitatory synapses during the third week.

This paper introduces a method using third-order correlations to fully characterize neural network activity, demonstrating its effectiveness across different data types.