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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Published on: May 8, 2021

Non-invasive brain-machine interface control with artificial intelligence copilots.

Johannes Y Lee¹, Sangjoon Lee¹, Abhishek Mishra¹

¹Dept of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90024, United States.

Biorxiv : the Preprint Server for Biology

|October 17, 2024

Summary

This summary is machine-generated.

This study introduces an AI-powered, non-invasive brain-machine interface (BMI) that enhances neural signal processing. This AI-BMI improves control for individuals with paralysis, overcoming previous performance limitations.

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Area of Science:

Neuroscience and Biomedical Engineering
Brain-Computer Interfaces (BCIs)
Artificial Intelligence in Medicine

Background:

Motor brain-machine interfaces (BMIs) aid individuals with paralysis but face clinical viability challenges.
Invasive BMIs offer high performance at the cost of neurosurgical risk.
Non-invasive BMIs lack surgical risk but suffer from lower performance and user frustration.

Purpose of the Study:

To develop high-performing, non-invasive BMIs by addressing the poor neural signal-to-noise ratio.
To break the performance-risk tradeoff inherent in current BMI technologies.
To create an "AI-BMI" system that integrates novel decoding with AI assistance.

Main Methods:

Developed a novel electroencephalography (EEG) decoding approach.
Integrated artificial intelligence (AI) copilots to infer task goals and assist action completion.
Employed an adaptive decoding strategy combining a convolutional neural network (CNN) and a ReFIT-like Kalman filter (KF).

Main Results:

Healthy users and a paralyzed participant demonstrated proficient control of computer cursors and robotic arms.
The AI copilot significantly improved goal acquisition speed by up to 4.3x in a standard cursor control task.
Enabled control of a robotic arm for a sequential pick-and-place task involving multiple blocks and locations.

Conclusions:

The developed AI-BMI system offers a promising non-invasive solution to enhance BMI performance.
This approach mitigates the need for neurosurgery while achieving proficient control.
Advancements in AI copilots may lead to clinically viable non-invasive AI-BMIs for broader adoption.