# An Open Source Simulator for Multi Degree-Of-Freedom Brain-Machine Interfaces

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $394,812

## Abstract

PROJECT SUMMARY
For millions with movement disorders including paralysis and ALS, intracortical brain-machine interfaces (BMIs)
are an emerging technology that aims to restore lost motor function and communication. The main component
of a BMI is a decoder algorithm that translates neural activity from motor areas of the brain into the kinematics
of a prosthetic device. Due to the complexity of these systems, which includes the BMI user interacting with the
decoded kinematics in a closed-feedback loop, current technology requires expensive and invasive experiments
to design, optimize, and validate decoder algorithms. The need for such experiments (1) results in slow develop-
ment and evaluation of decoder algorithms, and (2) limits the scope of people who can work on these problems
to a small group of nonhuman primate and clinical trial labs. As a consequence, BMIs have remained in pilot
clinical trials since their first reported demonstration in 2004.
We propose a new open-source simulator for multiple degree-of-freedom (DOF) BMI systems. The goals of this
simulator are to (1) reduce the time it takes to evaluate and optimize BMI algorithms from months to minutes,
and (2) significantly expand the community of researchers who develop testable algorithms for BMIs. To build
the simulator, we propose neural encoding models that generate synthetic motor cortical activity for multiple DOF
tasks. This is possible because neural population activity is relatively low-dimensional and has dynamics, which
can be learned via recurrent neural networks (RNNs). We build our neural simulators using data collected from
human clinical trials during point-to-point multi-DOF reaches. We also propose to develop new models of human
controllers. This solves an important problem in BMIs: users learn new control strategies when controlling a
particular BMI decoder algorithm. Our simulator uses deep imitation and reinforcement learning to solve this
problem. It is constrained through imitation learning to perform actions like a human. It is optimized through
reinforcement learning to explore new strategies – under the constraint of being human-like – to optimally control
the BMI. Together, we expect these innovations will result in a purely software simulator that accurately predicts
BMI performance and enables design and optimization. This tool will be open-sourced and available to all,
enabling widespread development of BMIs.

## Key facts

- **NIH application ID:** 10183995
- **Project number:** 1R01NS121097-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Jonathan Chau-Yan Kao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $394,812
- **Award type:** 1
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10183995

## Citation

> US National Institutes of Health, RePORTER application 10183995, An Open Source Simulator for Multi Degree-Of-Freedom Brain-Machine Interfaces (1R01NS121097-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10183995. Licensed CC0.

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