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

Abstract

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.

Fig. 1.

Schematic representation of the force field and workspace experiments. A: counterclockwise (CCW) and clockwise (CW) fields. Purple curve represents a typical 200-s hand path. Arrows represent the orientation of the fields. B: workspace experiment. Movements were made in the absence of any external force field but were confined to 1 of 2 smaller workspaces.

Fig. 2.

Tuning characteristics of biceps and triceps in monkeys A and F [right hemisphere (F_R)] during CCW (left) and CW (right) force fields. Blue line shows the average activity as a function of the direction of hand movement in 45° bins. Red line is the vector sum of the 8 averages, the length of which reflects both the discharge rate of the cell and the extent to which the tuning curve approximated a sinusoid. Green triangle denotes the edges of the 95% confidence interval of the preferred direction (PD) estimate. A and B: biceps and triceps recorded from monkey A. C and D: biceps and triceps recorded from monkey F_R. E: distribution of PD changes across all muscles recorded from both monkeys.

Fig. 3.

Within-task predictions of kinematics and EMG during the force field experiment. Data were recorded from monkey F_R during the random-target task in the CCW field. Normalized discharge rate for each of 39 neural signals (a combination of single and multiunit recordings) is shown by a color scale (top). Red rectangles on the position traces (bottom) indicate the size and the times of appearance and disappearance of the targets. Prediction accuracy in terms of R² is shown for just the plotted 20-s segment of each signal.

Fig. 4.

Summary quartile plots of within-task R² prediction performance for the force field experiment. A and B: data set A. C and D: data set F_R. Each box has lines at the lower quartile, median, and upper quartile. The whiskers extend 1.5 times the interquartile range within each box. Values outside this range are plotted as “+” symbols. The means across position (Pos), velocity (Vel), and EMG were 0.84/0.84/0.61 for data set A and 0.63/0.75/0.62 for data set F_R. Brd, brachioradialis; Tri_Lt, lateral head of triceps; Bic, biceps; Del_M, medial deltoid; Del_A, anterior deltoid; Del_P, posterior deltoid; Lat, latissimus dorsi; Pec_CL, clavicular head of pectoralis.

Fig. 5.

Relationship between R² prediction accuracy and EMG depth of modulation (DOM; see methods). Data from all experiments are combined, as indicated.

Fig. 6.

Summary quartile plots of across-task R² prediction performance for the force field experiment. A and B: data set A. C and D: data set F_R. All conventions as in Fig. 4. The means across position, velocity, and EMG were 0.75/0.47/0.47 for data set A and 0.54/0.31/0.49 for data set F_R.

Fig. 7.

Prediction of movement kinematics from EMG both within and across force field conditions. A: cross-validated predictions of both position and velocity signals using the full set of EMG signals as inputs and using training and testing data collected under a single force field condition. B: kinematic predictions made across force field conditions. The velocity-dependent fields prevented generalization of velocity predictions to a much greater extent than they did position.

Fig. 8.

Percent generalization for position, velocity, and EMG in the force field experiment. Percent generalization was calculated by dividing the across-task prediction accuracy by the corresponding within-task value. A: percent generalization computed with the R² metric. B: percent generalization computed with the mean squared error (MSE) metric. Error bars indicate SE.

Fig. 9.

Summary quartile plots of within-task R² prediction performance for the workspace experiment. A and B: data set A. C and D: data set F_R (right hemisphere). E and F: data set F_L (left hemisphere). All conventions as in Fig. 4. Means across position, velocity, and EMG were 0.74/0.81/0.58 for data set A, 0.67/0.77/0.59 for data set F_R, and 0.58/0.67/0.53 for data set F_L. Tri_Lg, long head of triceps.

Fig. 10.

Across-task predictions of kinematics and EMG during the workspace experiment. Monkey F was working in the far workspace, but predictions were made using decoders computed for the near workspace. In the far workspace, biceps and anterior deltoid were relatively long, while triceps and posterior deltoid were shortened.

Fig. 11.

Summary quartile plots of across-task R² prediction performance for the workspace experiment. A and B: data set A. C and D: data set F_R. E and F: data set F_L. Means across position, velocity, and EMG were 0.26/0.25/0.30 for data set A, 0.30/0.35/0.41 for data set F_R, and 0.27/0.36/0.40 for data set F_L.

Fig. 12.

Percent generalization for position, velocity, and EMG in the workspace experiment. A: percent generalization computed with the R² metric. B: percent generalization computed with the MSE metric. Error bars indicate SE.