NCT05470478

iBCI Optimization for Veterans With Paralysis

Enhancement and Optimization of a Mobile iBCI for Veterans With Paralysis

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 2 (estimated)
• Sponsor: VA Office of Research and Development · Federal
• Sex: All
• Age: 18 Years – 80 Years
• Healthy volunteers: Not accepted

Summary

VA research has been advancing a high-performance brain-computer interface (BCI) to improve independence for Veterans and others living with tetraplegia or the inability to speak resulting from amyotrophic lateral sclerosis, spinal cord injury or stoke. In this project, the investigators enhance deep learning neural network decoders and multi-state gesture decoding for increased accuracy and reliability and deploy them on a battery-powered mobile BCI device for independent use of computers and touch-enabled mobile devices at home. The accuracy and usability of the mobile iBCI will be evaluated with participants already enrolled separately in the investigational clinical trial of the BrainGate neural interface.

Detailed description

After VA IRB approval, this VA RR\&D study will engage participants in the BrainGate clinical trial (IDE, sponsor-investigator LR Hochberg). This study does not create a new clinical trial or modify the existing clinical trial as already listed on clinicaltrials.gov This project builds on a custom, mobile neural signal processing device with exceptional processing and low power characteristics, which has been developed through previous VA RR\&D funded research. This project takes advantage of the exceptional processing system, previously developed and validated, to create and quantify advanced neural decoding algorithms that show promise (in preclinical studies) for improving the accuracy and reliability of neural decoding - but that are likely too computationally demanding to be viable on existing real-time BCI systems. Decoding methods will include magnitude kinematic decoding with recursive neural networks and high-dimensional discrete gesture decoding. Computational methods to be evaluated include latent space methods and stable manifolds to improve day-to-day reliability of high performance and high-dimensional orthogonalization approaches to improve the independence of kinematic and gesture decoding.

Conditions

• Spinal Cord Injury
• Amyotrophic Lateral Sclerosis
• Brain Stem Infarctions
• Locked-in Syndrome
• Muscular Dystrophy

Interventions

Type	Name	Description
DEVICE	Mobile neural decoding platform (mobile iBCI)	An embedded neural signal processor device will be evaluated for its ability to provide accurate and reliable closed-loop control in a home-based brain-computer interface.

Timeline

• Start date: 2026-06-16
• Primary completion: 2027-06-30
• Completion: 2027-06-30
• First posted: 2022-07-22
• Last updated: 2026-02-23

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study