A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields

TY - JOUR

T1 - A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields

AU - Vato, Alessandro

AU - Szymanski, Francois D

AU - Semprini, Marianna

AU - Mussa-Ivaldi, Ferdinando A

AU - Panzeri, Stefano

PY - 2014/3/13

Y1 - 2014/3/13

N2 - We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

AB - We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

KW - Algorithms

KW - Brain-Computer Interfaces

KW - Calibration

KW - Humans

KW - Models, Statistical

KW - Motor Cortex/physiology

KW - Neurons/physiology

KW - Nonlinear Dynamics

KW - Normal Distribution

KW - Somatosensory Cortex/physiology

U2 - 10.1371/journal.pone.0091677

DO - 10.1371/journal.pone.0091677

M3 - SCORING: Journal article

C2 - 24626393

VL - 9

SP - e91677

JO - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 3

ER -