Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model

Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.

[1]  Paul Nuyujukian,et al.  Intention estimation in brain–machine interfaces , 2014, Journal of neural engineering.

[2]  Nicholas V. Annetta,et al.  Restoring cortical control of functional movement in a human with quadriplegia , 2016, Nature.

[3]  Jose M. Carmena,et al.  Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering , 2016, PLoS Comput. Biol..

[4]  A. Schwartz,et al.  High-performance neuroprosthetic control by an individual with tetraplegia , 2013, The Lancet.

[5]  Jose M. Carmena,et al.  Designing Dynamical Properties of Brain–Machine Interfaces to Optimize Task-Specific Performance , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Robert E. Kass,et al.  Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control , 2010, Journal of Computational Neuroscience.

[7]  Robert E Kass,et al.  Statistical issues in the analysis of neuronal data. , 2005, Journal of neurophysiology.

[8]  Vikash Gilja,et al.  A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. , 2011, Journal of neurophysiology.

[9]  Nicolas Y. Masse,et al.  Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface , 2015, Science Translational Medicine.

[10]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[11]  L R Hochberg,et al.  Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Byron M. Yu,et al.  Learning an Internal Dynamics Model from Control Demonstration , 2013, ICML.

[13]  Ziv M. Williams,et al.  A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design , 2013, PloS one.

[14]  Nicholas G. Hatsopoulos,et al.  Brain-machine interface: Instant neural control of a movement signal , 2002, Nature.

[15]  Byron M. Yu,et al.  Motor cortical control of movement speed with implications for brain-machine interface control. , 2014, Journal of neurophysiology.

[16]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[17]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[18]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[19]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[20]  Christine H. Blabe,et al.  Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law , 2017, Journal of neural engineering.

[21]  Stephen I. Ryu,et al.  A High-Performance Keyboard Neural Prosthesis Enabled by Task Optimization , 2015, IEEE Transactions on Biomedical Engineering.

[22]  Miri Benyamini,et al.  Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces , 2015, Front. Syst. Neurosci..

[23]  Hongwei Mao,et al.  Decoding arm speed during reaching , 2018, Nature Communications.

[24]  John P. Cunningham,et al.  A High-Performance Neural Prosthesis Enabled by Control Algorithm Design , 2012, Nature Neuroscience.

[25]  Stephen I. Ryu,et al.  A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models , 2017, IEEE Transactions on Biomedical Engineering.

[26]  Amar R. Marathe,et al.  The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices , 2015, Journal of neural engineering.

[27]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[28]  Sagi Perel,et al.  Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics. , 2015, Journal of neurophysiology.

[29]  Anish A. Sarma,et al.  Clinical translation of a high-performance neural prosthesis , 2015, Nature Medicine.

[30]  Francis R. Willett,et al.  High performance communication by people with paralysis using an intracortical brain-computer interface , 2017, eLife.

[31]  Shaomin Zhang,et al.  Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex , 2014, Journal of neural engineering.

[32]  Michael J. Black,et al.  Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia , 2008, Journal of neural engineering.

[33]  M. Nicolelis,et al.  Unscented Kalman Filter for Brain-Machine Interfaces , 2009, PloS one.

[34]  Byron M. Yu,et al.  Neural constraints on learning , 2014, Nature.

[35]  S. Meagher.  Instant neural control of a movement signal , 2002 .

[36]  John D. Simeral,et al.  A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain–Computer Interfaces , 2018, IEEE Transactions on Biomedical Engineering.

[37]  J. M. Carmena,et al.  Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Lee E Miller,et al.  Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface , 2016, Journal of neural engineering.

[39]  Per B. Sederberg,et al.  Meeting brain–computer interface user performance expectations using a deep neural network decoding framework , 2018, Nature Medicine.

[40]  David Sussillo,et al.  A recurrent neural network for closed-loop intracortical brain–machine interface decoders , 2012, Journal of neural engineering.

[41]  Robert E. Kass,et al.  2009 Special Issue: Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms , 2009 .

[42]  Matthew T. Kaufman,et al.  The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type , 2016, eNeuro.

[43]  Francis R. Willett,et al.  Restoration of reaching and grasping in a person with tetraplegia through brain-controlled muscle stimulation: a proof-of-concept demonstration , 2017, The Lancet.

[44]  David Sussillo,et al.  Making brain–machine interfaces robust to future neural variability , 2016, Nature communications.

[45]  Lakshminarayan Srinivasan,et al.  Stochastic Optimal Control as a Theory of Brain-Machine Interface Operation , 2013, Neural Computation.

[46]  Nicolas Y. Masse,et al.  Advantages of closed-loop calibration in intracortical brain–computer interfaces for people with tetraplegia , 2013, Journal of neural engineering.

[47]  Chethan Pandarinath,et al.  Feedback control policies employed by people using intracortical brain–computer interfaces , 2017, Journal of neural engineering.