
NSF Org: |
TI Translational Impacts |
Recipient: |
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Initial Amendment Date: | February 19, 2019 |
Latest Amendment Date: | February 19, 2019 |
Award Number: | 1843859 |
Award Instrument: | Standard Grant |
Program Manager: |
Henry Ahn
hahn@nsf.gov (703)292-7069 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
Start Date: | February 15, 2019 |
End Date: | December 31, 2020 (Estimated) |
Total Intended Award Amount: | $224,999.00 |
Total Awarded Amount to Date: | $224,999.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
4620 Henry St Pittsburgh PA US 15213-3715 (412)613-6652 |
Sponsor Congressional District: |
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Primary Place of Performance: |
4620 Henry Street Pittsburgh PA US 15213-3715 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | STTR Phase I |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.084 |
ABSTRACT
This SBIR Phase I project will advance novel techniques for developing high-resolution noninvasive brain imaging systems, capable of recording unprecedented spatiotemporal resolution inferences of the brain activity for a portable system. These systems leverage novel fundamental analysis and results, and experimental demonstrations, that show that spatial resolution of Electroencephalography (EEG) is not saturated at densities of a few hundred electrodes, unlike what has been widely believed in clinical and neuroscience community. They also build on recent work by the PIs that enables faster and reliable acquisition of EEG signals. The success of the proposed work will help diagnose worsening brain injuries before the injury occurs. Brain injuries affect 1.7 million Americans every year. Commercially, it will enable higher resolution brain-machine interfaces for applications such as virtual reality interfacing and neuroprostheses, generating novel avenues for jobs and revenue through creation of an entirely new industry. This transdisciplinary effort brings together neuroscientists and engineers and the concepts developed in this effort will inform material for basic neuroscience and neuroengineering courses. The team will continue to publish and publicize their work at clinical conferences. One of the core employees will be a minority female who has contributed to the research, guided several other minority (and non-minority) female students, and now wants to lead the development end of this project.
This effort builds a systematic platform that challenges the widely held belief that increasing electrode-densities of EEG to beyond a few hundred electrodes does not improve spatial resolution. There are several problems with the traditional EEG that the platform overcomes: (i) typically only 9-32 electrodes are used for clinical diagnoses and these are fundamentally limited to only providing poor spatial resolution; (ii) because traditional EEGs have low resolution, surgical treatments often require invasive procedures. E.g., for diagnosing severity or worsening of Traumatic Brain Injuries (TBI) by measuring parameters of cortical spreading depolarizations (CSDs), which are mediators of worsening brain injuries; (iii) long-term EEG measurements are cumbersome, and high-density systems can take hours of manual labor to install. The PIs' preliminary work provides strong evidence supporting the claim that ultra-high-density EEG will provide the first non-invasive and portable modality for high spatial and temporal resolution brain imaging. Novel brain-imaging algorithms will be developed and benchmarked against existing techniques and assessed using novel fundamental limits. Novel techniques will be deployed in the design of conductive sponges and in lowering power, enabling the platform to be portable and usable over long term. These improvements will be rigorously tested through simulations, experiments, and real data analysis.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project focused on the development of instrumentation and algorithms for Ultra-resolution electroencephalography (EEG) to detect Cortical Spreading Depolarizations as an indicator of severe and worsening traumatic brain injury. The project included both hardware components and software algorithms.
Our quick-apply cap was designed to allow a technician to apply EEG electrodes to a patient?s head more quickly than the standard method of individually-applied electrodes. We've designed a frame with modular electrode holders that can be removed in areas where recent scalp surgery or trauma has occured. This cap can be used to apply standard EEG (21 electrodes) or ultra-resolution EEG by building it with individual electrode modules or modules with clusters of electrodes. We've designed a novel, patent-pending sponge material that holds moisture better than other sponges, and creates low-resistance contact with the patient's scalp to produce higher quality signal data. Our team also designed electronic amplifiers and digital converters that can be placed right at the site of each electrode, allowing data to be collected without the added noise found in long wires.
We also created algorithms to detect cortical spreading depolarizations (CSD) non-invasively from scalp EEG. All previous recordings of these waves of depressed activity in the brain have been made invasively, by placing electrodes directly onto the brain surface. Our algorithm detects and tracks ?wavefronts? of a CSD wave, and stitches together data across space and time to make a detection. As a follow-up to this algorithm and to further improve the spatio-temporal accuracy of non-invasive CSD detection, we have explored deep learning algorithms as well. In an ongoing project, we are applying deep learning-based image processing algorithms to track CSD wavefronts in the extracted 2D images from the scalp potentials.
Progress made towards the development of our ultra-resolution EEG system during the Phase I STTR period is directly translatable to the manufacturing and production of our quick-apply EEG device with the standard number of electrodes, and to a follow-on product with higher resolution to detect CSDs and allow better diagnosis and treatment of traumatic brain injury.
Last Modified: 01/25/2021
Modified by: Shawn K Kelly
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