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

By C. Pandarinath, Paul Nuyujukian, C. Blabe, B. Sorice, J. Saab, Francis R. Willett, Leigh R. Hochberg, Krishna V. Shenoy, Jaimie M. Henderson. eLife, 2017

494 citations recorded by the source corpus.

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 DOI: http://dx.doi.org/10.7554/eLife.18554.001

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/817574eedd1acbeddb5d802fd7122e90a285d483

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In Cellular and Molecular Neuroscience

  1. Krishna V. Shenoy
  2. Ganesh R. Naik
  3. Leigh R. Hochberg
  4. John P. Donoghue

In Cognitive Neuroscience

  1. Krishna V. Shenoy
  2. Leigh R. Hochberg
  3. Jaimie M. Henderson
  4. Ganesh R. Naik
  5. Francis R. Willett
  6. Donald T. Avansino
  7. John P. Cunningham
  8. John P. Donoghue

In Human-Computer Interaction

  1. Ahsan Habib Khandoker

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