Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

Autor: Pun TK; Biomedical Engineering Graduate Program, School of Engineering, Brown University, Providence, RI, USA. tsam_kiu_pun@brown.edu.; School of Engineering, Brown University, Providence, RI, USA. tsam_kiu_pun@brown.edu.; Carney Institute for Brain Science, Brown University, Providence, RI, USA. tsam_kiu_pun@brown.edu., Khoshnevis M; Division of Applied Mathematics, Brown University, Providence, RI, USA., Hosman T; School of Engineering, Brown University, Providence, RI, USA.; VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA., Wilson GH; Department of Neurosurgery, Stanford University, Stanford, CA, USA., Kapitonava A; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA., Kamdar F; Department of Neurosurgery, Stanford University, Stanford, CA, USA., Henderson JM; Department of Neurosurgery, Stanford University, Stanford, CA, USA.; Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA., Simeral JD; School of Engineering, Brown University, Providence, RI, USA.; Carney Institute for Brain Science, Brown University, Providence, RI, USA.; VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA., Vargas-Irwin CE; Carney Institute for Brain Science, Brown University, Providence, RI, USA.; VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA.; Department of Neuroscience, Brown University, Providence, RI, USA., Harrison MT; Carney Institute for Brain Science, Brown University, Providence, RI, USA.; Division of Applied Mathematics, Brown University, Providence, RI, USA., Hochberg LR; School of Engineering, Brown University, Providence, RI, USA.; Carney Institute for Brain Science, Brown University, Providence, RI, USA.; VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA.; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.; Department of Neurology, Harvard Medical School, Boston, MA, USA.
Jazyk: angličtina
Zdroj: Communications biology [Commun Biol] 2024 Oct 21; Vol. 7 (1), pp. 1363. Date of Electronic Publication: 2024 Oct 21.
DOI: 10.1038/s42003-024-06784-4
Abstrakt: Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.
(© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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