iBCI Optimization for Veterans With Paralysis

VA Office of Research and Development

Status: Not yet enrolling

Conditions: Locked-in Syndrome; Brain Stem Infarctions; Amyotrophic Lateral Sclerosis; Muscular Dystrophy; Spinal Cord Injury

Treatments: Device: Mobile neural decoding platform (mobile iBCI)

Study type: Interventional

Funder types: Other U.S. Federal agency

Identifiers

NCT05470478

I01RX003803 (Other Grant/Funding Number)

A3803-R

Details and patient eligibility

About

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.

Full 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.

Enrollment: 2 estimated patients

Sex: All

Ages: 18 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Inclusion criteria are extensive and are determined by the associated BrainGate IDE(clinicaltrials.gov # NCT00912041)
• Informally, participants will be tetraplegic or anarthric with little or no functional use of the arms and legs

Exclusion criteria

• Exclusion criteria are extensive and are determined by the associated BrainGate IDE(clinicaltrials.gov # NCT00912041).

Trial design

Primary purpose: Other

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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2 participants in 1 patient group

Evaluation of an enhanced iBCI

Experimental group

Description:

Performance of new decoding algorithms and methods will be developed and embedded in a small, mobile neural processor. The utility of these will be assessed separately with participants in the BrainGate pilot clinical trial, IDE.

Treatment:

Device: Mobile neural decoding platform (mobile iBCI)