Data from: The impact of task context on predicting finger movements in a brain-machine interface

A key factor in the clinical translation of brain-machine interfaces
(BMIs) for restoring hand motor function will be their robustness to
changes in a task. With functional electrical stimulation (FES) for
example, the patient’s own hand will be used to produce a wide range of
forces in otherwise similar movements. To investigate the impact of task
changes on BMI performance, we trained two rhesus macaques to control a
virtual hand with their physical hand while we added springs to each
finger group (index or middle-ring-small) or altered their wrist posture.
Using simultaneously recorded intracortical neural activity, finger
positions, and electromyography, we found that predicting finger
kinematics and finger-related muscle activations across contexts led to
significant increases in prediction error, especially for muscle
activations. However, with respect to online BMI control of the virtual
hand, changing either training task context or the hand’s physical context
during online control had little effect on online performance. We explain
this dichotomy by showing that the structure of neural population activity
remained similar in new contexts, which could allow for fast adjustment
online. Additionally, we found that neural activity shifted trajectories
proportional to the required muscle activation in new contexts, possibly
explaining biased kinematic predictions and suggesting a feature that
could help predict different magnitude muscle activations while producing
similar kinematics.