6 papers
Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning
Stella Ho, Joel Villalobos, Joseph West +7
ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of vis…
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…