Kinetic Trajectory Decoding Using Motor Cortical Ensembles

Abstract

Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion such as hand position and velocity, it is known that motor cortical activity also correlates with kinetic signals, including active hand force and joint torque. Here, we attempted to reconstruct torque trajectories of the shoulder and elbow joints from the activity of simultaneously recorded units in primary motor cortex (MI) as monkeys (Macaca Mulatta) made reaching movements in the horizontal plane. Using a linear filter decoding approach that considers the history of neuronal activity up to one second in the past, we found torque reconstruction performance nearly equal to that of Cartesian hand position and velocity, despite the considerably greater bandwidth of the torque signals. Moreover, the addition of delayed position and velocity feedback to the torque decoder substantially improved the torque reconstructions, suggesting that simple limb-state feedback may be useful to optimize BMI performance. These results may be relevant for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.

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

“…Ironically, one of the earliest multielectrode studies concluded that force and its first derivative were encoded more prominently in M1 than were movement kinematics (Humphrey et al 1970). A recent study found that torque computed for the shoulder and elbow joints during planar reaching movements was predicted only slightly less accurately than joint angular position or velocity (Fagg et al 2009). A direct comparison of end point kinematics and grip force found the latter to be predicted dramatically better (Carmena et al 2003), although EMG was predicted less accurately in that study.…”

“…In parallel, a small number of groups have begun to develop BMI applications based on the control of kinetic variables. One group found accuracy in joint torque predictions that was nearly comparable to kinematic predictions during planar reaching (Fagg et al 2009). Another study achieved predictions of electromyogram (EMG) activity that were of similar or greater accuracy than those typical of kinematic studies (Pohlmeyer et al 2007b).…”

“…While correlations exist between M1 and both kinematic and kinetic signals, it is not clear which, if any, may represent the "true" signal type encoded by M1. Experimentally, numerous attempts have been made to dissociate kinematic and kinetic signals by adding loads to the limb (Evarts 1968;Fagg et al 2009;Gandolfo et al 2000;Gribble and Scott 2002;Kalaska et al 1989) or by changing the posture of the limb to alter its mechanics (Caminiti et al 1990;Kakei et al 1999;Morrow et al 2007; Scott and Kalaska 1997). Taken together, these studies indicate that the discharge of many, but not all, M1 neurons is influenced by posture and external load.…”

Local field potentials allow accurate decoding of muscle activity

“…For each file, we trained the Wiener cascade decoder on 9 min of data and tested it on the remaining minute. As a measure of performance, we calculated the mean fractional variance accounted for (VAF) between actual and decoded EMG over all 10 folds as follows (Fagg et al 2009):…”

Mixing decoded cursor velocity and position from an offline Kalman filter improves cursor control in people with tetraplegia

“…One of the first successful closed-loop demonstrations decoded cursor position [7]. Since then, joint torques [8], angles [9] as well as grip action [10] have also been estimated.…”