A5001783557- EEG and Brain-Computer Interfaces
- Neonatal and fetal brain pathology
- Stroke Rehabilitation and Recovery
- Epilepsy research and treatment
- Context-Aware Activity Recognition Systems
- Obstructive Sleep Apnea Research
Georgia Institute of Technology
2022-2024
Abstract Background Seizure detection is challenging outside the clinical environment due to lack of comfortable, reliable, and practical long-term neurophysiological monitoring devices. We developed a novel, discreet, unobstructive in-ear sensing system that enables electroencephalography (EEG) recording. This first study we are aware systematically compares seizure utility EEG with simultaneously recorded intracranial EEG. In addition, present similar comparison between scalp Methods this...
Obtaining medical data using wearable sensors is a potential replacement for in-hospital monitoring, but the lack of such poses challenge development. One solution recordings to boost performance via transfer learning. While there are many possible learning algorithms, few have been tested in domain EEG-based sleep staging. Furthermore, ways determining which method will work best besides exhaustive testing. Measures transferability do exist, typically used selection pre-trained models...
At-home sleep staging using wearable medical sensors poses a viable alternative to in-hospital polysomnography due its lower cost and disruption the daily lives of patients, especially in case long-term monitoring. Machine learning with wearables however is difficult paucity data from sensors, making automation challenge. Transfer hospital polysomnograms can boost performance, but still hindered by differences between EEG resulting part differing electrode placement. We improve transfer...