Smoothness as a failure mode of Bayesian mixture models in brain-machine interfaces.

Various recursive Bayesian filters based on reach state equations (RSE) have been proposed to convert neural signals into reaching movements in brain-machine interfaces. When the target is known, RSE produce exquisitely smooth trajectories relative to the random walk prior in the basic Kalman filter. More realistically, the target is unknown, and gaze analysis or other side information is expected to provide a discrete set of potential targets.

Dynamic analysis of naive adaptive brain-machine interfaces.

The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements.