# Long-term reliable neuroprosthetic control of a robotic arm and hand using electrocorticography.

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $655,924

## Abstract

PROJECT SUMMARY
Multiple neurological diseases [e.g. spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), brain
stem stroke] can all result in severe and devastating limb paralysis. A recent comprehensive
assessment found that >200,000 patients suffer from tetraplegia or severe tetraparesis that prevents
completion of basic activities of daily living that require arm and hand functions. Surveys of such
patients have indicated that improvement of arm and hand function is a top priority. There are no
current therapies or assistive devices that can aid patients with tetraplegia or severe tetraparesis to
experience restoration of reaching and grasping functionality. Our proposal aims to test methods to
enable such patients to directly control a complex robotic arm and hand with the capacity to perform a
set of clinically relevant tasks.
Our specific goals are to leverage the stability of ECoG to establish robust robotic control that is stable
across a period of at least 8 weeks without need for recalibration. Our published data along with new
preliminary data supports the notion that ECoG signals can allow a paralyzed individual to learn
complex neuroprosthetic control that requires no additional training. We will compare two decoding
methods and their ability to enable long-term stable ‘plug-and-play’ complex control. We then aim to
further boost robustness of real-world control in two ways. First, we will track fluctuations in neural
states to reduce decoding errors; this is key for long-term continuous accurate control. Second, we will
test a system that can assist with pre-shaping the robot during neuroprosthetic control.
Together, our aims will determine the feasibility of complex control of neuroprosthetic technology in a
target population of paralyzed patients with severe disability. We will determine how well ECoG can
enable stable and intuitive control of a robotic arm and hand that can enable reaching, grasping and
flexible manipulation of objects. We strongly believe that demonstration of these outcomes will drive
the field towards clinically viable neuroprosthetic control and thereby dramatically improve the quality of
life for paralyzed patients.

## Key facts

- **NIH application ID:** 10804205
- **Project number:** 1R01HD111562-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Edward Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $655,924
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10804205

## Citation

> US National Institutes of Health, RePORTER application 10804205, Long-term reliable neuroprosthetic control of a robotic arm and hand using electrocorticography. (1R01HD111562-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10804205. Licensed CC0.

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