Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface

In the research of motion control using brain-machine interface (BMI), analysis is usually conducted on one ensemble of neurons whose activity serves as direct input to the BMI decoder (control units). The number of control units is diverse in different control modes. That is to say, the size of dimensions of neural signals used in motion control is diverse. However, how will the behavioral performance change with this kind of diversity? What effects does this diversity have on modulation characteristics of control units? To answer these questions, we designed three modes of motion tasks using neural signals with different dimension sizes to control. Our results imply that as the dimension reduces, some deviations appear in behavioral performance. At the same time, the control units tend to have a directional division of control, then enhance their stability and increase modulations after division.

[1]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[2]  Eva L. Dyer,et al.  Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces , 2018, The Journal of Neuroscience.

[3]  A. Seth.  Causal connectivity of evolved neural networks during behavior. , 2005, Network.

[4]  Nicholas G Hatsopoulos,et al.  The science of neural interface systems. , 2009, Annual review of neuroscience.

[5]  Brain–machine interfaces: Restoring movement in a paralysed hand , 2012, Nature Reviews Neuroscience.

[6]  Anil K. Seth,et al.  The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference , 2014, Journal of Neuroscience Methods.

[7]  A. Schwartz,et al.  Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. , 2012, Journal of neurophysiology.

[8]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[9]  Byron M. Yu,et al.  Learning by neural reassociation , 2018, Nature Neuroscience.

[10]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  Steven M Chase,et al.  Distinct types of neural reorganization during long-term learning. , 2019, Journal of neurophysiology.

[12]  Jose M. Carmena,et al.  Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control , 2017, Neuron.

[13]  Byron M. Yu,et al.  New neural activity patterns emerge with long-term learning , 2019, Proceedings of the National Academy of Sciences.

[14]  Adrienne L Fairhall,et al.  Reconfiguring Motor Circuits for a Joint Manual and BCI Task , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  A. P. Georgopoulos,et al.  Primate motor cortex and free arm movements to visual targets in three- dimensional space. II. Coding of the direction of movement by a neuronal population , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[16]  K. Shenoy,et al.  Neural Population Dynamics Underlying Motor Learning Transfer , 2018, Neuron.

[17]  J. Kalaska,et al.  Learning to Move Machines with the Mind , 2022 .

[18]  Dragan F. Dimitrov,et al.  Reversible large-scale modification of cortical networks during neuroprosthetic control , 2011, Nature Neuroscience.