I-Corps: Translation Potential of a Wearable, Brain-computer Interface System for In-home Stroke Rehabilitation
- Institution
- University of Texas at Austin, TX
- Principal investigator
- Huiliang Wang
- Funding
- $50K
- Duration
- 2024–2025
Moonbase score
Still being scored
Abstract
The broader impact of this I-Corps project is the development of a novel stroke rehabilitation system, achieved through the introduction of a wearable electrode system designed for in-home use. Stroke, a leading cause of disability, places substantial economic burdens on individuals and healthcare systems. The project's innovative approach could empower stroke survivors to engage in rehabilitation within the comfort of their homes. This technology could not only enhance patient outcomes but also help address the economic challenges associated with stroke disabilities. This project could also signify a critical advancement at the intersection of technology and healthcare, with the potential to improve the landscape of stroke rehabilitation by making it more inclusive, convenient, and economically viable for a broader segment of the population. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a wearable system designed for in-home stroke rehabilitation, integrating an electrode system embedded with an electroencephalography (EEG)-based brain-computer interfaces (BCI). The components include a stretchable mesh headset featuring durable electrodes, a wireless system resilient to interference, and intelligent machine learning algorithms facilitating unsupervised brain-computer interface operation. By addressing the challenges associated with conventional gel-based electrodes and the necessity for expert supervision, this technology represents a novel advancement in neural recording capabilities. The fusion of brain-computer interface technology, wireless communication, and machine learning algorithms could not only make stroke rehabilitation more accessible, convenient, and technologically advanced but also holds promise for diagnosing and treating a variety of neurological disorders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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