Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics

Abstract

Cherian A, Krucoff MO, Miller LE. Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics. J Neurophysiol 106: 564 -575, 2011. First published May 11, 2011 doi:10.1152/jn.00553.2010.-During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.electromyogram; force field; limb movement; posture REACHING IS a deceptively simple action. We are typically aware only of the relatively straight, smoothly accelerating movement of the hand toward a target.

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

“…The true language of the motor cortex, or how the motor cortex encodes its output signals, is a subject of debate (Vargas-Irwin et al, 2010; Cherian et al, 2011). Coordinated actions of the limbs may engage widespread cortical areas, and M1 is known as the site where motor plans tend to merge before diverging to multiple muscle groups (Vargas-Irwin et al, 2010).…”

“…Coordinated actions of the limbs may engage widespread cortical areas, and M1 is known as the site where motor plans tend to merge before diverging to multiple muscle groups (Vargas-Irwin et al, 2010). While M1 clearly contains kinematic information (joint position and trajectory) sufficient for accurate predictions (Vargas-Irwin et al, 2010), there is evidence that it may more directly encode kinetic (force) variables (Morrow et al, 2007; Cherian et al, 2011; Flint et al, 2014). This would suggest that BMIs built to encode force and/or EMG signals may be more robust across different positional dynamics than trajectory-based BMIs.…”

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

“…However, we have shown previously that isometric wrist EMG predicted with an optimal linear filter decoder generalized better across forearm posture than did a decoder similarly trained to predict endpoint force [33]. We have made similar observations for reaching movements across different arm postures and external loads [62]. If the actual mapping between M1 activity and EMG were linear, an EMG decoder trained with a robust data set should generalize well.…”

Brain-controlled muscle stimulation for the restoration of motor function

“…The limited information rate of EEG is note sufficient to predict EMG, but both intracortical LFP (Flint et al, 2012a) and electrocorticograms recorded from the surface of cortex (Shin et al, 2012) have been used successfully. Furthermore, we have shown for both reaching (Cherian et al, 2011) and wrist movements (Oby et al, 2013), that the mapping from M1 discharge to muscle activity remains more stable across altered posture and external dynamics, than does the corresponding mapping to movement kinematics. If prediction of muscle activity can provide control signals that are easy to learn, are robust across postural conditions, and provide direct control of limb dynamics, it is an approach that should be considered seriously along side the much more common kinematic interface.…”