A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces

IEEE Trans Biomed Eng. 2018 Sep;65(9):2066-2078. doi: 10.1109/TBME.2017.2783358. Epub 2017 Dec 14.


Objective: Recent reports indicate that making better assumptions about the user's intended movement can improve the accuracy of decoder calibration for intracortical brain-computer interfaces. Several methods now exist for estimating user intent, including an optimal feedback control model, a piecewise-linear feedback control model, ReFIT, and other heuristics. Which of these methods yields the best decoding performance?

Methods: Using data from the BrainGate2 pilot clinical trial, we measured how a steady-state velocity Kalman filter decoder was affected by the choice of intention estimation method. We examined three separate components of the Kalman filter: dimensionality reduction, temporal smoothing, and output gain (speed scaling).

Results: The decoder's dimensionality reduction properties were largely unaffected by the intention estimation method. Decoded velocity vectors differed by <5% in terms of angular error and speed vs. target distance curves across methods. In contrast, the smoothing and gain properties of the decoder were greatly affected (> 50% difference in average values). Since the optimal gain and smoothing properties are task-specific (e.g. lower gains are better for smaller targets but worse for larger targets), no one method was better for all tasks.

Conclusion: Our results show that, when gain and smoothing differences are accounted for, current intention estimation methods yield nearly equivalent decoders and that simple models of user intent, such as a position error vector (target position minus cursor position), perform comparably to more elaborate models. Our results also highlight that simple differences in gain and smoothing properties have a large effect on online performance and can confound decoder comparisons.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Brain-Computer Interfaces*
  • Calibration
  • Computer Simulation
  • Female
  • Humans
  • Intention*
  • Male
  • Middle Aged
  • Models, Neurological
  • Motor Cortex / physiology*
  • Movement / physiology
  • Quadriplegia / rehabilitation
  • Signal Processing, Computer-Assisted*