# Next generation brain-machine interfaces controlled synergistically with artificial intelligence

> **NIH NIH DP2** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $2,340,000

## Abstract

PROJECT SUMMARY
Over 5 million people in the USA – nearly 1 person in 50 – live with a form of paralysis due to causes including
stroke, spinal cord injury, multiple sclerosis, and ALS. People who have lost the ability to move also lose a
profound sense of control, freedom, and independence in their lives. But paralysis does not take away one's
intent or desire to move; the brain still encodes these thoughts through neural signals. Brain-machine interfaces
(BMIs) aim to restore the ability to communicate with the world by translating these neural signals into actions.
BMIs decode neural signals into the movements of a computer cursor on a screen or a robotic arm, allowing the
user to interact with the world autonomously.
While BMIs have existed for over two decades, they have remained in pilot clinical trials dating back to 2004 and
have not achieved widespread use. The key reason for this is that BMI performance has not achieved levels of
performance that overcome their costs and risks. This is true for both invasive BMIs requiring neurosurgery, as
well as for non-invasive BMIs, which can be used without surgical procedures but achieve low performance.
This proposal aims to dramatically increase the quality of life for millions with paralysis by making widely accessi-
ble BMIs available within the next five years. To achieve this groundbreaking goal, there needs to be a paradigm
shift in the way BMIs fundamentally operate. Revolutionary next generation BMIs making significant impact must
be designed to: (1) achieve categorically unprecedented performance not possible with current BMI systems
(e.g., over an order of magnitude improvement) and (2) minimize cost to patients, ideally being non-invasive. En-
tirely novel BMI architectures are needed to fundamentally transcend a performance vs cost trade-off, achieving
excellent performance at lower risks and costs.
To meet this need, I propose to develop a next-generation BMI where the user and an artificial intelligence (AI)
agent synergistically cooperate. We term this an “AI-BMI.” The AI agent predicts the user's intended motor actions
and synergistically helps to complete them. Critically, the AI agent aids the precise and detailed execution of the
user's intended movements, augmenting performance. By doing so, this architecture fundamentally changes
the design objectives of BMIs from neural decoding of precise movements (difficult) to behavioral and neural
inference of the user's intent (easier). Non-invasive AI-BMIs, if successful, would be transformative, enabling
next generation BMI technology that allows people with paralysis to once again move, but mitigates medical risks
associated with neurosurgery and lowers system costs.

## Key facts

- **NIH application ID:** 10003004
- **Project number:** 1DP2NS122037-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Jonathan Chau-Yan Kao
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,340,000
- **Award type:** 1
- **Project period:** 2020-09-30 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003004

## Citation

> US National Institutes of Health, RePORTER application 10003004, Next generation brain-machine interfaces controlled synergistically with artificial intelligence (1DP2NS122037-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10003004. Licensed CC0.

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