title: Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia.
abstract: OBJECTIVE
  Brain-computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using 'open-loop' (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for 'closed-loop' (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing.
  
  
  METHODS
  Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data.
  
  
  RESULTS
  Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements.
  
  
  CONCLUSIONS
  Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional 'adaptive' advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.
authors.affiliation: Department of Neuroscience, Brown University, 2 Stimson Ave., Box 1994, Providence, RI 02912, USA. beata@brown.edu
authors.forename: Beata
authors.initials: B
authors.lastname: Jarosiewicz
authors.forename: Nicolas Y
authors.initials: NY
authors.lastname: Masse
authors.forename: Daniel
authors.initials: D
authors.lastname: Bacher
authors.forename: Sydney S
authors.initials: SS
authors.lastname: Cash
authors.forename: Emad
authors.initials: E
authors.lastname: Eskandar
authors.forename: Gerhard
authors.initials: G
authors.lastname: Friehs
authors.forename: John P
authors.initials: JP
authors.lastname: Donoghue
authors.forename: Leigh R
authors.initials: LR
authors.lastname: Hochberg
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000592:standards*; D062207:Brain-Computer Interfaces / Q000592:standards*; D002138:Calibration; D025461:Feedback, Physiological; D005260:Female; D006801:Humans; D007092:Imagination; D008875:Middle Aged; D009044:Motor Cortex / Q000503:physiopathology*; D009068:Movement; D011782:Quadriplegia / Q000503:physiopathology* / Q000534:rehabilitation*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D014481:United States