title: Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces.
abstract: Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to "click" during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as "non-click" those periods that the kinematic decoder's retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of "retrospectively labeled" decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.
authors.affiliation: Neuroscience, Brown University, Providence, RI 02912, United States; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States. Electronic address: beataj@stanford.edu.
authors.forename: Beata
authors.initials: B
authors.lastname: Jarosiewicz
authors.affiliation: Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States; School of Engineering, Brown University, Providence, RI 02912, United States.
authors.forename: Anish A
authors.initials: AA
authors.lastname: Sarma
authors.affiliation: School of Engineering, Brown University, Providence, RI 02912, United States; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States.
authors.forename: Jad
authors.initials: J
authors.lastname: Saab
authors.affiliation: Neurology, Massachusetts General Hospital, Boston, MA 02114, United States.
authors.forename: Brian
authors.initials: B
authors.lastname: Franco
authors.affiliation: Neurology, Massachusetts General Hospital, Boston, MA 02114, United States; Neurology, Harvard Medical School, Boston, MA 02115, United States.
authors.forename: Sydney S
authors.initials: SS
authors.lastname: Cash
authors.affiliation: Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States; Neuroscience, Harvard Medical School, Boston, MA 02115, United States.
authors.forename: Emad N
authors.initials: EN
authors.lastname: Eskandar
authors.affiliation: Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Veterans Affairs Medical Center, Providence, RI 02908, United States; Brown Institute for Brain Science, Brown University, Providence, RI 02912, United States; School of Engineering, Brown University, Providence, RI 02912, United States; Neurology, Massachusetts General Hospital, Boston, MA 02114, United States; Neurology, Harvard Medical School, Boston, MA 02115, United States.
authors.forename: Leigh R
authors.initials: LR
authors.lastname: Hochberg
mesh_terms: D062207:Brain-Computer Interfaces / Q000592:standards; D002138:Calibration; D003201:Computers / Q000592:standards; D006801:Humans
keywords: Adaptive classification; Amyotrophic lateral sclerosis (ALS); Augmentative and assistive communication (AAC); Brain-Machine Interface (BMI); Spinal cord injury (SCI); Stroke