activity
20192022
most citedTransfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures

21 citations · 28 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.NC20226 cited

Intracranial EEG structure-function coupling predicts surgical outcomes in focal epilepsy

Nishant Sinha, John S. Duncan, Beate Diehl +9

Alterations to structural and functional brain networks have been reported across many neurological conditions. However, the relationship between structure and function -- their co…

q-bio.NC20211 cited

Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy

Gabrielle M. Schroeder, Fahmida A. Chowdhury, Mark J. Cook +5

A seizure's electrographic dynamics are characterised by its spatiotemporal evolution, also termed dynamical "pathway" and the time it takes to complete that pathway, which results…

cs.CV202121 cited

Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures

Fernando Pérez-García, Catherine Scott, Rachel Sparks +2

Detailed analysis of seizure semiology, the symptoms and signs which occur during a seizure, is critical for management of epilepsy patients. Inter-rater reliability using qualitat…

q-bio.NC2020

Band power modulation through intracranial EEG stimulation and its cross-session consistency

Christoforos A Papasavvas, Gabrielle M Schroeder, Beate Diehl +3

Background: Direct electrical stimulation of the brain through intracranial electrodes is currently used to probe the epileptic brain as part of pre-surgical evaluation, and it is…

q-bio.NC2019

Interictal intracranial EEG for predicting surgical success: the importance of space and time

Yujiang Wang, Nishant Sinha, Gabrielle M. Schroeder +8

Predicting post-operative seizure freedom using functional correlation networks derived from interictal intracranial EEG has shown some success. However, there are important challe…