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Sci Transl Med. 2015 Nov 11;7(313):313ra179. doi: 10.1126/scitranslmed.aac7328.

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

Author information

1
Department of Neuroscience, Brown University, Providence, RI 02912, USA. Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. beata@brown.edu.
2
Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. School of Engineering, Brown University, Providence, RI 02912, USA.
3
Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA.
4
Department of Neuroscience, Brown University, Providence, RI 02912, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA.
5
Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. School of Engineering, Brown University, Providence, RI 02912, USA. Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
6
Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
7
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
8
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA. Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
9
School of Engineering, Brown University, Providence, RI 02912, USA. Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
10
Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA. Department of Neurology, Harvard Medical School, Boston, MA 02115, USA.
11
Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02115, USA.
12
Neurosurgery, Rhode Island Hospital, Providence, RI 02903, USA.
13
Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA. Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
14
Stanford Neurosciences Institute, Stanford University, Stanford, CA 94305, USA. Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. Department of Neurobiology, Stanford University, Stanford, CA 94305, USA. Department of Bioengineering, Stanford University, Stanford, CA 94305, USA. Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
15
Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Department of Neuroscience, Brown University, Providence, RI 02912, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. School of Engineering, Brown University, Providence, RI 02912, USA.
16
Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA. Brown Institute for Brain Science, Brown University, Providence, RI 02912, USA. School of Engineering, Brown University, Providence, RI 02912, USA. Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA. Department of Neurology, Harvard Medical School, Boston, MA 02115, USA.

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. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated 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.

PMID:
26560357
PMCID:
PMC4765319
DOI:
10.1126/scitranslmed.aac7328
[Indexed for MEDLINE]
Free PMC Article

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