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The hand, a complex effector comprising dozens of degrees of freedom ofmovement, endows us with the ability to flexibly, precisely, and effortlesslyinteract with objects. The neural signals associated with dexterous handmovements in primary motor cortex (M1) and somatosensory cortex (SC) havereceived comparatively less attention than have those that are associated withproximal limb control. To fill this gap, we trained three monkeys to graspobjects varying in size, shape and orientation while tracking their handpostures and recording single-unit activity from M1 and SC. We then decodedtheir hand kinematics across 30 joints from population activity in these areas.We found that we could accurately decode kinematics with a small number ofneural signals and that performance was higher for decoding joint angles thanjoint angular velocities, in contrast to what has been found with proximal limbdecoders. We conclude that cortical signals can be used for dexterous handcontrol in brain machine interface applications and that posturalrepresentations in SC may be exploited via intracortical stimulation to closethe sensorimotor loop.

Decoding hand kinematics from population responses in sensorimotor cortex during grasping