Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published October 2019 | Published
Journal Article Open

A Robust Encoding Scheme for Delivering Artificial Sensory Information via Direct Brain Stimulation

Abstract

Innovations for creating somatosensation via direct electrical stimulation of the brain will be required for the next generation of bi-directional cortical neuroprostheses. The current lack of tactile perception and proprioceptive input likely imposes a fundamental limit on speed and accuracy of brain-controlled prostheses or re-animated limbs. This study addresses the unique challenge of identifying a robust, high bandwidth sensory encoding scheme in a high-dimensional parameter space. Previous studies demonstrated single dimensional encoding schemes delivering low bandwidth sensory information, but no comparison has been performed across parameters, nor with update rates suitable for real-time operation of a neuroprosthesis. Here, we report the first comprehensive measurement of the resolution of key stimulation parameters such as pulse amplitude, pulse width, frequency, train interval and number of pulses. Surprisingly, modulation of stimulation frequency was largely undetectable. While we initially expected high frequency content to be an ideal candidate for passing high throughput sensory signals to the brain, we found only modulation of very low frequencies were detectable. Instead, the charge-per-phase of each pulse yields the highest resolution sensory signal, and is the key parameter modulating perceived intensity. The stimulation encoding patterns were designed for high-bandwidth information transfer that will be required for bi-directional brain interfaces. Our discovery of the stimulation features which best encode perceived intensity have significant implications for design of any neural interface seeking to convey information directly to the brain via electrical stimulation.

Additional Information

© 2019 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. Manuscript received April 11, 2019; revised July 31, 2019; accepted August 9, 2019. Date of publication August 21, 2019; date of current version October 8, 2019. This work was supported in part by the Center for Neurotechnology, a National Science Foundation Engineering Research Center (EEC-028725), and in part by the Washington Research Foundation UW Institute for Neuroengineering. The authors acknowledge Joseph O'Doherty for discussions about data interpretation.

Attached Files

Published - 08809212.pdf

Files

08809212.pdf
Files (3.1 MB)
Name Size Download all
md5:deb962137ec83a1af149a6f05c92bc11
3.1 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023