# Neurophysiologically-informed Design of Flexible, 2-learner Brain-Machine Interfaces for Robust and Skillful Performance

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $343,378

## Abstract

PROJECT SUMMARY
This proposal aims to elucidate the computational and neural basis of neuroprosthetic skill learning by
leveraging recent advances in the science and engineering of closed-loop brain-machine interfacing. The
outcome of the proposed work has the potential to guide the development of the next generation of
neurophysiologically-informed, cortically-controlled neuroprosthetic systems for patients with neurological
disorders. State-of-the-art brain-machine interfaces (BMIs) leverage machine learning to rapidly calibrate to the
neural activity of individuals, but performance also benefits from subjects learning to reliably produce desired
neural activity patterns. The basic science and engineering principles of designing such a “2-learner BMI” in
which the brain and machine synergistically learn are not well understood. Hence, this proposal aims to
investigate how the brain learns when the machine undergoes different degrees of learning, how different
degrees of brain learning affect long-term BMI performance, robustness, and generalization, and how these
principles can guide the design of a 2-learner BMI system which facilitates brain learning. The proposal is
structured in three aims: 1) To study the impact of decoder adaptation on the development of neural encoding
models underlying neuroprosthetic skill; 2) To Study how decoder adaptation and resultant neural encoding
model influences BMI performance with perturbations (robustness) and BMI performance on unpracticed tasks
(generalization); and 3) Design and validation of the next-generation Flexible 2-Learner Decoder architecture.
The analyses and experiments proposed in these aims will leverage the fundamental knowledge gained about
how the brain learns and acquires neuroprosthetic skills into the neurophysiologically-informed design of robust
and high-performance closed-loop motor neuroprosthetics that generalize to new tasks.

## Key facts

- **NIH application ID:** 10113682
- **Project number:** 5R01NS106094-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Jose Miguel Carmena
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $343,378
- **Award type:** 5
- **Project period:** 2018-03-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10113682

## Citation

> US National Institutes of Health, RePORTER application 10113682, Neurophysiologically-informed Design of Flexible, 2-learner Brain-Machine Interfaces for Robust and Skillful Performance (5R01NS106094-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10113682. Licensed CC0.

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