Literature DB >> 28220753

High performance communication by people with paralysis using an intracortical brain-computer interface.

Chethan Pandarinath1,2,3,4,5, Paul Nuyujukian1,3,6,7, Christine H Blabe1, Brittany L Sorice8, Jad Saab9,10,11, Francis R Willett12,13, Leigh R Hochberg8,9,10,11,14, Krishna V Shenoy2,3,6,15,16,17, Jaimie M Henderson1,3.   

Abstract

Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.

Entities:  

Keywords:  ALS; assistive technology; brain-machine interface; human; human biology; medicine; neural prosthesis; neuroscience

Mesh:

Year:  2017        PMID: 28220753      PMCID: PMC5319839          DOI: 10.7554/eLife.18554

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  56 in total

1.  Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain.

Authors:  G Townsend; V Platsko
Journal:  J Neural Eng       Date:  2016-02-25       Impact factor: 5.379

2.  Performance sustaining intracortical neural prostheses.

Authors:  Paul Nuyujukian; Jonathan C Kao; Joline M Fan; Sergey D Stavisky; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neural Eng       Date:  2014-10-13       Impact factor: 5.379

3.  Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients.

Authors:  Gabriel Pires; Urbano Nunes; Miguel Castelo-Branco
Journal:  Clin Neurophysiol       Date:  2012-01-13       Impact factor: 3.708

4.  A high-performance brain-computer interface.

Authors:  Gopal Santhanam; Stephen I Ryu; Byron M Yu; Afsheen Afshar; Krishna V Shenoy
Journal:  Nature       Date:  2006-07-13       Impact factor: 49.962

Review 5.  Challenges and opportunities for next-generation intracortically based neural prostheses.

Authors:  Vikash Gilja; Cindy A Chestek; Ilka Diester; Jaimie M Henderson; Karl Deisseroth; Krishna V Shenoy
Journal:  IEEE Trans Biomed Eng       Date:  2011-01-20       Impact factor: 4.538

6.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.

Authors:  Lynn M McCane; Susan M Heckman; Dennis J McFarland; George Townsend; Joseph N Mak; Eric W Sellers; Debra Zeitlin; Laura M Tenteromano; Jonathan R Wolpaw; Theresa M Vaughan
Journal:  Clin Neurophysiol       Date:  2015-02-07       Impact factor: 3.708

7.  A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces.

Authors:  John P Cunningham; Paul Nuyujukian; Vikash Gilja; Cindy A Chestek; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neurophysiol       Date:  2010-10-13       Impact factor: 2.714

8.  A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.

Authors:  F Nijboer; E W Sellers; J Mellinger; M A Jordan; T Matuz; A Furdea; S Halder; U Mochty; D J Krusienski; T M Vaughan; J R Wolpaw; N Birbaumer; A Kübler
Journal:  Clin Neurophysiol       Date:  2008-06-20       Impact factor: 3.708

9.  Brain Painting: First Evaluation of a New Brain-Computer Interface Application with ALS-Patients and Healthy Volunteers.

Authors:  Jana I Münßinger; Sebastian Halder; Sonja C Kleih; Adrian Furdea; Valerio Raco; Adi Hösle; Andrea Kübler
Journal:  Front Neurosci       Date:  2010-11-22       Impact factor: 4.677

10.  Rapid control and feedback rates enhance neuroprosthetic control.

Authors:  Maryam M Shanechi; Amy L Orsborn; Helene G Moorman; Suraj Gowda; Siddharth Dangi; Jose M Carmena
Journal:  Nat Commun       Date:  2017-01-06       Impact factor: 14.919

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  85 in total

1.  A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.

Authors:  Samuel R Nason; Alex K Vaskov; Matthew S Willsey; Elissa J Welle; Hyochan An; Philip P Vu; Autumn J Bullard; Chrono S Nu; Jonathan C Kao; Krishna V Shenoy; Taekwang Jang; Hun-Seok Kim; David Blaauw; Parag G Patil; Cynthia A Chestek
Journal:  Nat Biomed Eng       Date:  2020-07-27       Impact factor: 25.671

2.  A rodent brain-machine interface paradigm to study the impact of paraplegia on BMI performance.

Authors:  Nathaniel R Bridges; Michael Meyers; Jonathan Garcia; Patricia A Shewokis; Karen A Moxon
Journal:  J Neurosci Methods       Date:  2018-05-31       Impact factor: 2.390

Review 3.  Human visual skills for brain-computer interface use: a tutorial.

Authors:  Melanie Fried-Oken; Michelle Kinsella; Betts Peters; Brandon Eddy; Bruce Wojciechowski
Journal:  Disabil Rehabil Assist Technol       Date:  2020-06-01

4.  Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.

Authors:  David M Brandman; Michael C Burkhart; Jessica Kelemen; Brian Franco; Matthew T Harrison; Leigh R Hochberg
Journal:  Neural Comput       Date:  2018-09-14       Impact factor: 2.026

Review 5.  Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.

Authors:  Chethan Pandarinath; K Cora Ames; Abigail A Russo; Ali Farshchian; Lee E Miller; Eva L Dyer; Jonathan C Kao
Journal:  J Neurosci       Date:  2018-10-31       Impact factor: 6.167

6.  The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.

Authors:  Kristin M Quick; Jessica L Mischel; Patrick J Loughlin; Aaron P Batista
Journal:  J Neurophysiol       Date:  2018-06-27       Impact factor: 2.714

7.  Intracortical Microstimulation Modulates Cortical Induced Responses.

Authors:  Mathias Benjamin Voigt; Prasandhya Astagiri Yusuf; Andrej Kral
Journal:  J Neurosci       Date:  2018-07-27       Impact factor: 6.167

8.  Frequency Shifts and Depth Dependence of Premotor Beta Band Activity during Perceptual Decision-Making.

Authors:  Chandramouli Chandrasekaran; Iliana E Bray; Krishna V Shenoy
Journal:  J Neurosci       Date:  2019-01-03       Impact factor: 6.167

9.  Speech synthesis from ECoG using densely connected 3D convolutional neural networks.

Authors:  Miguel Angrick; Christian Herff; Emily Mugler; Matthew C Tate; Marc W Slutzky; Dean J Krusienski; Tanja Schultz
Journal:  J Neural Eng       Date:  2019-03-04       Impact factor: 5.379

Review 10.  Clinical neuroprosthetics: Today and tomorrow.

Authors:  Morgan B Lee; Daniel R Kramer; Terrance Peng; Michael F Barbaro; Charles Y Liu; Spencer Kellis; Brian Lee
Journal:  J Clin Neurosci       Date:  2019-07-30       Impact factor: 1.961

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