Kinematic decoding

Kinematic decoding maps neural activity onto continuous movement variables — position, velocity, acceleration, and sometimes force or joint angles — rather than onto discrete class labels. In motor cortex the dominant and most reliably decodable variable is velocity: population activity correlates more tightly with the direction and speed of intended movement than with static position, which is why most cursor and arm decoders estimate velocity and integrate it to obtain position. Directly decoding position tends to drift and to produce sluggish, offset control.

The choice of kinematic representation shapes everything downstream. A velocity representation gives responsive, self-correcting control but accumulates integration error; adding position as a state with a smoothness prior stabilizes holds; decoding higher-order or redundant kinematics (acceleration, endpoint plus orientation plus grasp) expands the controllable degrees of freedom at the cost of harder estimation and more calibration data. Kinematic decoders can be linear (population vector, optimal linear estimator, Wiener filter), recursive (Kalman variants), or nonlinear (recurrent and other neural networks).