Abstract
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Highlights
Brain-computer interfaces (BCIs), have the potential to substantially increase quality of life for people suffering from motor disorders, including paralysis and amputation
Many brain-computer interfaces (BCIs) use activity from motor and/or premotor cortex to achieve continuous control. These BCIs can be viewed from a feedback control perspective, as the motor field has done for the native limb: the brain is the controller of a new plant, defined by the BCI
Our designs are informed by a feedback model, which assumes the user develops a volitional control signal to achieve a goal given the current state of the world
Summary
In this model, the neural prosthetic user has a high level task goal, g, such as positioning the prosthetic in a specific region of the workspace. The user develops a volitional control signal xV (e.g. velocity) by assessing the current state of the workspace, w, and applying a strategy, V, towards achieving g. There are behavioral and cognitive signals that are not directly controlled in the task (e.g. eye position, level of attention), which we will operationally refer to as taskunconstrained signals. The volitional control signal, xV, and task-unconstrained signals, xV , give rise to y, the neural firing recorded by the prosthetic, through M. XV is used by S to update w, for example the position of the neural prosthetic in the workspace may be changed.
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