A5009457413- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Atrial Fibrillation Management and Outcomes
- ECG Monitoring and Analysis
- Gaze Tracking and Assistive Technology
- Epilepsy research and treatment
- Muscle activation and electromyography studies
- Neural dynamics and brain function
New York University
2022-2023
Technical University of Denmark
2022-2023
Austrian Institute for Health Technology Assessment GmbH
2023
Abstract Objective Effective surgical treatment of drug‐resistant epilepsy depends on accurate localization the epileptogenic zone (EZ). High‐frequency oscillations (HFOs) are potential biomarkers EZ. Previous research has shown that HFOs often occur within submillimeter areas brain tissue and coarse spatial sampling clinical intracranial electrode arrays may limit capture HFO activity. In this study, we sought to characterize microscale activity captured thin, flexible...
One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30-70% continue experience seizures following resection. Surgical outcomes may be improved with more accurate localization tissue. We have previously developed novel thin-film, subdural electrode arrays hundreds microelectrodes over a 100-1000 mm2 area enable high-resolution mapping neural activity. Here, we used these high-density study microscale...
Motor Imagery (MI) based Brain Computer Inter-face (BCI) is a promising neurorehabilitation tool for treating motor impaired stroke survivors. It enables the MI electroencephalogram (EEG) signals to be converted/mapped into customized robotic and assisting commands. Even though causes varying effects in brain, EEG have shown promises towards classification of post-stroke subjects. This paper presents left right wrist dorsiflexion performed on 6 stoke The data are recorded from 16 electrode...
<p>Atrial fibrillation (AF) is a common cardiac arrhythmia causing severe complications if left untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory electrocardiogram (ECG) screening. Recently, deep learning (DL) has gained prominence in analysing long-term ECG and automating AF detection. However, like any medical classification problem, obtaining diverse labelled data for DL model training expensive time-consuming. This paper proposes...
<p>Atrial fibrillation (AF) is a common cardiac arrhythmia causing severe complications if left untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory electrocardiogram (ECG) screening. Recently, deep learning (DL) has gained prominence in analysing long-term ECG and automating AF detection. However, like any medical classification problem, obtaining diverse labelled data for DL model training expensive time-consuming. This paper proposes...