Jerome Engel

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

This study identifies subregional cerebellar atrophy patterns in epilepsy syndromes using MRI data from 1,602 patients and 1,022 controls, finding significant volume loss in posterior lobe regions.

This paper develops a deep learning algorithm to detect and classify epileptogenic high-frequency oscillations (eHFOs) from intracranial EEG data with high accuracy.

The paper analyzes the DOWN state in rats during immobility, showing local and generalized slow oscillations in prefrontal cortex and hippocampus with distinct neuronal correlates.