The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we investigated this question by recording high-density electrocorticography from the motor and premotor cortices of seven human subjects performing a sequential movement-force motor task. We decoded finger movement (0.7±0.3 fractional variance account for; FVAF) and force (0.7±0.2 FVAF) with high accuracy, yet found different spatial representations. We also found clear distinctions in electrocorticographic activity by using deep learning methods to uncover state-space representations, and by developing a new metric, the neural vector angle. Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90±6%). Thus, finger movement and force have distinct representations in motor/premotor cortices. This will inform our understanding of the neural control of movement as well as the design of grasp brain-machine interfaces.
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