title: The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices.
abstract: OBJECTIVE
  Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain.
  
  
  METHODS
  To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains.
  
  
  RESULTS
  Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present.
  
  
  CONCLUSIONS
  This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.
authors.affiliation: Department of Neurosciences, The Cleveland Clinic, Cleveland, OH 44195, USA. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA. Cleveland Functional Electrical Stimulation (FES) Center of Excellence, Louis Stokes VA Medical Center, Cleveland, OH 44106, USA. Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA.
authors.forename: A R
authors.initials: AR
authors.lastname: Marathe
authors.forename: D M
authors.initials: DM
authors.lastname: Taylor
mesh_terms: D062207:Brain-Computer Interfaces; D004735:Energy Transfer; D056228:Feedback, Sensory / Q000502:physiology*; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008297:Male; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology*