5 papers
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…
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…
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation
John S. Russo, Thomas A. Shiels, Chin-Hsuan Sophie Lin +2
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This…