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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Active tactile exploration using a brain-machine-brain interface.

Joseph E O'Doherty¹, Mikhail A Lebedev, Peter J Ifft

¹Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.

|October 7, 2011

Summary

This summary is machine-generated.

This study introduces a brain-machine-brain interface (BMBI) that enables prosthetic arms to provide artificial tactile feedback. This breakthrough could restore a sense of touch for users of neuroprosthetics.

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Area of Science:

Neuroscience
Biomedical Engineering
Robotics

Background:

Brain-machine interfaces (BMIs) enable communication between the brain and external devices, but lack tactile sensation.
Restoring sensorimotor function is a key goal for limb neuroprosthetics.

Purpose of the Study:

To develop and test a brain-machine-brain interface (BMBI) that controls actuator movement and provides artificial tactile feedback.
To investigate the potential of intracortical microstimulation (ICMS) for conveying tactile information.

Main Methods:

A BMBI was developed using neuronal activity from the primary motor cortex to control an actuator (virtual arm).
Artificial tactile feedback was delivered via ICMS to the primary somatosensory cortex when the actuator interacted with virtual objects.
Neuronal recordings and ICMS were temporally multiplexed to prevent signal interference.

Main Results:

Monkeys successfully used the BMBI to control a virtual arm and perform an object exploration task.
ICMS successfully conveyed artificial tactile properties of virtual objects, allowing monkeys to distinguish between them.
The system demonstrated effective temporal multiplexing of neural recordings and ICMS.

Conclusions:

BMBI technology can successfully integrate motor control with artificial tactile feedback.
ICMS is a viable method for generating artificial somatic perceptions in neuroprosthetics.
This approach holds promise for enhancing the functionality of clinical motor neuroprostheses by adding sensory feedback.