1 code implementation • 24 Nov 2022 • Teja Gupta, Neeraj Wagh, Samarth Rawal, Brent Berry, Gregory Worrell, Yogatheesan Varatharajah
The current clinical EEG review process relies heavily on expert visual review, which is unscalable and error-prone.
no code implementations • NeurIPS 2021 • Krishnakant V. Saboo, Anirudh Choudhary, Yurui Cao, Gregory Worrell, David T Jones, Ravi Iyer
We model Alzheimer’s disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge.
no code implementations • 15 Dec 2018 • Yogatheesan Varatharajah, Brent Berry, Jan Cimbalnik, Vaclav Kremen, Jamie Van Gompel, Matt Stead, Benjamin Brinkmann, Ravishankar Iyer, Gregory Worrell
An ability to map seizure-generating brain tissue, i. e., the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy.
1 code implementation • Neuroinformatics 2018 • Petr Nejedly, Jan Cimbalnik, Petr Klimes, Filip Plesinger, Josef Halamek, Vaclav Kremen, Ivo Viscor, Benjamin H. Brinkmann, Martin Pail, Milan Brazdil, Gregory Worrell, Pavel Jurak
We show that the proposed technique can be used as a generalized model for iEEG artifact detection.
Ranked #1 on
EEG Artifact Removal
on MayoClinic_iEEG
no code implementations • NeurIPS 2017 • Yogatheesan Varatharajah, Min Jin Chong, Krishnakant Saboo, Brent Berry, Benjamin Brinkmann, Gregory Worrell, Ravishankar Iyer
This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG).