2 citations · 4 across the 13 of their papers we have counts for
5 papers · 1 filter
Adaptive Segmentation of EEG for Machine Learning Applications
Johnson Zhou, Joseph West, Krista A. Ehinger +3
Objective. Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for machine learning, the signal mu…
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG
Timothy B Mahoney, JingYang Liu, Huakun Xin +2
Individuals with severe physical disabilities often experience diminished quality of life stemming from limited ability to engage with their surroundings. Brain-Computer Interface…
Sub-Scalp EEG for Sensorimotor Brain-Computer Interface
Timothy B Mahoney, David B Grayden, Sam E John
Objective: To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating it…
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface
Suleman Rasheed, James Bennett, Peter E. Yoo +2
An Oculomotor Brain-Computer Interface (BCI) records neural activity from regions of the brain involved in planning eye movements and translates this activity into control commands…
Sub-Scalp Brain-Computer Interface Device Design and Fabrication
Timothy B. Mahoney, David B. Grayden, Sam E. John
Current brain-computer interfaces (BCI) face limitations in signal acquisition. While sub-scalp EEG offers a potential solution, existing devices prioritize chronic seizure monitor…