Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface

Beata Jarosiewicz, Anish A. Sarma, Daniel Bacher, Nicolas Y. Masse, John D. Simeral, Brittany Sorice, Erin M. Oakley, Christine Blabe, Chethan Pandarinath, Vikash Gilja, Sydney S. Cash, Emad N. Eskandar, Gerhard Friehs, Jaimie M. Henderson, Krishna V. Shenoy, John P. Donoghue, Leigh R. Hochberg

Research output: Contribution to journalArticle

84 Citations (Scopus)

Abstract

Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs.Wedemonstrate that signal nonstationarity in an intracortical BCI can bemitigated automatically in software, enabling long periods (hours to days) of self-paced point-And-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

Original languageEnglish (US)
Article number313ra179
JournalScience Translational Medicine
Volume7
Issue number313
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Fingerprint

Brain-Computer Interfaces
Quadriplegia
Self-Help Devices
Paralysis
Software
Communication

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Jarosiewicz, B., Sarma, A. A., Bacher, D., Masse, N. Y., Simeral, J. D., Sorice, B., ... Hochberg, L. R. (2015). Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Science Translational Medicine, 7(313), [313ra179]. https://doi.org/10.1126/scitranslmed.aac7328

Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. / Jarosiewicz, Beata; Sarma, Anish A.; Bacher, Daniel; Masse, Nicolas Y.; Simeral, John D.; Sorice, Brittany; Oakley, Erin M.; Blabe, Christine; Pandarinath, Chethan; Gilja, Vikash; Cash, Sydney S.; Eskandar, Emad N.; Friehs, Gerhard; Henderson, Jaimie M.; Shenoy, Krishna V.; Donoghue, John P.; Hochberg, Leigh R.

In: Science Translational Medicine, Vol. 7, No. 313, 313ra179, 01.01.2015.

Research output: Contribution to journalArticle

Jarosiewicz, B, Sarma, AA, Bacher, D, Masse, NY, Simeral, JD, Sorice, B, Oakley, EM, Blabe, C, Pandarinath, C, Gilja, V, Cash, SS, Eskandar, EN, Friehs, G, Henderson, JM, Shenoy, KV, Donoghue, JP & Hochberg, LR 2015, 'Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface', Science Translational Medicine, vol. 7, no. 313, 313ra179. https://doi.org/10.1126/scitranslmed.aac7328
Jarosiewicz, Beata ; Sarma, Anish A. ; Bacher, Daniel ; Masse, Nicolas Y. ; Simeral, John D. ; Sorice, Brittany ; Oakley, Erin M. ; Blabe, Christine ; Pandarinath, Chethan ; Gilja, Vikash ; Cash, Sydney S. ; Eskandar, Emad N. ; Friehs, Gerhard ; Henderson, Jaimie M. ; Shenoy, Krishna V. ; Donoghue, John P. ; Hochberg, Leigh R. / Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. In: Science Translational Medicine. 2015 ; Vol. 7, No. 313.
@article{63879d0d2e9f473c9b2938066fae7e0f,
title = "Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface",
abstract = "Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs.Wedemonstrate that signal nonstationarity in an intracortical BCI can bemitigated automatically in software, enabling long periods (hours to days) of self-paced point-And-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.",
author = "Beata Jarosiewicz and Sarma, {Anish A.} and Daniel Bacher and Masse, {Nicolas Y.} and Simeral, {John D.} and Brittany Sorice and Oakley, {Erin M.} and Christine Blabe and Chethan Pandarinath and Vikash Gilja and Cash, {Sydney S.} and Eskandar, {Emad N.} and Gerhard Friehs and Henderson, {Jaimie M.} and Shenoy, {Krishna V.} and Donoghue, {John P.} and Hochberg, {Leigh R.}",
year = "2015",
month = "1",
day = "1",
doi = "10.1126/scitranslmed.aac7328",
language = "English (US)",
volume = "7",
journal = "Science Translational Medicine",
issn = "1946-6234",
publisher = "American Association for the Advancement of Science",
number = "313",

}

TY - JOUR

T1 - Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface

AU - Jarosiewicz, Beata

AU - Sarma, Anish A.

AU - Bacher, Daniel

AU - Masse, Nicolas Y.

AU - Simeral, John D.

AU - Sorice, Brittany

AU - Oakley, Erin M.

AU - Blabe, Christine

AU - Pandarinath, Chethan

AU - Gilja, Vikash

AU - Cash, Sydney S.

AU - Eskandar, Emad N.

AU - Friehs, Gerhard

AU - Henderson, Jaimie M.

AU - Shenoy, Krishna V.

AU - Donoghue, John P.

AU - Hochberg, Leigh R.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs.Wedemonstrate that signal nonstationarity in an intracortical BCI can bemitigated automatically in software, enabling long periods (hours to days) of self-paced point-And-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

AB - Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs.Wedemonstrate that signal nonstationarity in an intracortical BCI can bemitigated automatically in software, enabling long periods (hours to days) of self-paced point-And-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

UR - http://www.scopus.com/inward/record.url?scp=84947997221&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947997221&partnerID=8YFLogxK

U2 - 10.1126/scitranslmed.aac7328

DO - 10.1126/scitranslmed.aac7328

M3 - Article

VL - 7

JO - Science Translational Medicine

JF - Science Translational Medicine

SN - 1946-6234

IS - 313

M1 - 313ra179

ER -