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Related Experiment Video

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Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes

Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes

Published on: June 8, 2022

Performance sustaining intracortical neural prostheses.

Paul Nuyujukian¹, Jonathan C Kao, Joline M Fan

¹Department of Bioengineering, Stanford University, Stanford, CA. School of Medicine, Stanford University, Stanford, CA. Department of Neurosurgery, Stanford University, Stanford, CA.

Journal of Neural Engineering

|October 14, 2014

Summary

This summary is machine-generated.

Researchers developed a stable neural prosthesis (brain-machine interface) that maintains high performance despite signal aging. This brain-machine interface minimizes retraining time, making it more practical for restoring communication and movement in paralysis.

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Area of Science:

Neuroscience
Biomedical Engineering
Rehabilitation Technology

Background:

Neural prostheses aim to restore function for individuals with paralysis.
Aging signal sources pose a significant challenge to the robustness of brain-machine interfaces.
Minimizing retraining time is crucial for the practical application of neural prostheses.

Purpose of the Study:

To develop a neural prosthesis capable of sustained high performance with aging neural signals.
To minimize retraining time required for neural prosthesis operation.
To investigate methods for maintaining decoder stability in brain-machine interfaces.

Main Methods:

Two rhesus macaques with intracortical microelectrode arrays (implanted 1-4 years prior) were trained on a neurally-controlled cursor task.
Performance was quantified using achieved bitrate (bits per second, bps).
Sustained performance was evaluated over contiguous days and extended periods.

Main Results:

A fixed decoder achieved 4 bps in a monkey with a younger implant, the highest reported communication rate.
This decoder maintained performance over 22 months with a minimal decline rate of 0.24 bps yr(-1).
In a monkey with an older implant, performance was sustained for two weeks without retraining by using prior data, attributed to neural tuning fluctuation cancellation.

Conclusions:

The study documents the highest-performing communication neural prosthesis in non-human primates to date.
Fixed decoder stability was confirmed and extended, crucial for long-term brain-machine interface use.
A protocol for system stability was demonstrated, enhancing the practicality of neural prostheses by reducing training demands.