# Enhancement and optimization of a mobile iBCI for Veterans with paralysis

> **NIH VA I01** · PROVIDENCE VA  MEDICAL CENTER · 2022 · —

## Abstract

Intracortical brain-computer interfaces (iBCIs) record and process neural signals streaming from
arrays of electrodes implanted in the cortex to enable fast, accurate and intuitive control of
assistive technologies for individuals living with paralysis arising from spinal cord injury, stroke,
or amyotrophic lateral sclerosis (ALS). Using an intracortical BCI, people with tetraplegia have
been able to use their imagined hand movements to command point-and-click actions on a
computer, type with a virtual keyboard, use communication apps such as chat, and browse the
web. Imagined movements have also been used to control assistive devices including the DEKA
prosthetic arm, assistive robotic arms and even one’s own paralyzed limb through patterned
electrical stimulation of paralyzed muscles. Recent development of a miniature wireless signal
transmitter and a wireless, compact, battery-operated neural signal processor has raised the
potential for individuals with severe motor disability to use a wheelchair-mounted iBCI
independently at home without technical assistance. To be a viable assistive technology, the
iBCI must be not only mobile but also high-performance, reliable, and intuitive to use. This
research enhances all of these aspects of a mobile iBCI by translating algorithmic innovations
demonstrated in varied pre-clinical studies and optimizing them toward stable, high-performance
decoding in a mobile iBCI. This research first transforms a highly accurate and responsive
kinematic neural decoder (a deep learning recursive neural network) to run on the mobile iBCI’s
computationally powerful embedded hardware. To help stabilize kinematic decoding over time,
enhance performance, and ease calibration requirements, this research then looks to theories of
intrinsic neural manifolds to adapt dimensionality reduction (DR) techniques to high-
dimensional, multiscale human neural data. Next, state-of-the-art data science approaches are
integrated with multiclass analyses to promote reliable, accurate classification of a large set of
discrete hand gestures imagined by iBCI users. Next, DR methods are evaluated to disentangle
simultaneous kinematic and gesture decoding for smoother, more accurate and unperturbed
iBCI control. These cumulative approaches will be translated to embedded hardware form to run
on the powerful mobile processor to provide on-demand control of mobile and touch-enabled
devices using both mouse-like movements and gestures (such as swipe-to-scroll and pinch-to
zoom). Mapping unique gestures to additional functions will instantly activate key shortcuts or
gesture-to-phrase output. Using this wheelchair-mounted iBCI, a speech-disabled individual
could imagine a hand gesture to generate a text-to-speech greeting or call for help. Overall, this
research leverages state-of-the-art machine learning innovations toward a more capable,
reliable, and versatile iBCI to promote independence for people with severe motor disability.

## Key facts

- **NIH application ID:** 10538008
- **Project number:** 1I01RX003803-01A2
- **Recipient organization:** PROVIDENCE VA  MEDICAL CENTER
- **Principal Investigator:** John David Simeral
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538008

## Citation

> US National Institutes of Health, RePORTER application 10538008, Enhancement and optimization of a mobile iBCI for Veterans with paralysis (1I01RX003803-01A2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10538008. Licensed CC0.

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