Rapid calibration of an intracortical brain-computer interface for people with tetraplegia

David M. Brandman, Tommy Hosman, Jad Saab, Michael C. Burkhart, Benjamin E. Shanahan, John G. Ciancibello, Anish A. Sarma, Daniel J. Milstein, Carlos E. Vargas-Irwin, Brian Franco, Jessica Kelemen, Christine Blabe, Brian A. Murphy, Daniel R. Young, Francis R. Willett, Chethan Pandarinath, Sergey D. Stavisky, Robert F. Kirsch, Benjamin L. Walter, A. Bolu AjiboyeSydney S. Cash, Emad N. Eskandar, Jonathan P. Miller, Jennifer A. Sweet, Krishna V. Shenoy, Jaimie M. Henderson, Beata Jarosiewicz, Matthew T. Harrison, John D. Simeral, Leigh R. Hochberg

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


Objective. Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration. Approach. We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF). Main results. Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration. Significance. These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.

Original languageEnglish (US)
Article number026007
JournalJournal of neural engineering
Issue number2
StatePublished - Jan 24 2018
Externally publishedYes


  • Gaussian process regression
  • Kalman filtering
  • brain-computer interfaces
  • discriminative Kalman filter
  • neural decoding

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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