High-resolution neural recordings improve the accuracy of speech decoding

Suseendrakumar Duraivel, Shervin Rahimpour, Chia-Han Chiang, Michael Trumpis, Charles Wang, Katrina Barth, Stephen C. Harward, Shivanand P. Lad, Allan H. Friedman, Derek G. Southwell, Saurabh R. Sinha, Jonathan Viventi () and Gregory B. Cogan ()
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Suseendrakumar Duraivel: Duke University
Shervin Rahimpour: Duke School of Medicine
Chia-Han Chiang: Duke University
Michael Trumpis: Duke University
Charles Wang: Duke University
Katrina Barth: Duke University
Stephen C. Harward: Duke School of Medicine
Shivanand P. Lad: Duke School of Medicine
Allan H. Friedman: Duke School of Medicine
Derek G. Southwell: Duke University
Saurabh R. Sinha: University of Pennsylvania
Jonathan Viventi: Duke University
Gregory B. Cogan: Duke University

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.

Date: 2023
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DOI: 10.1038/s41467-023-42555-1

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