Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.

Objective. Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex (PPC) to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex.Approach. A quadriplegic human subject was implanted with multi-electrode recording arrays in the PPC. Neural spiking and local field potentials were recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person movement generation task in addition to closed loop control performance.Main results. We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and 'attached' 3D arm relative to either a visually similar but 'detached' 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors.Significance. In human parietal cortex, single neuron activity and local field potentials responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, this tuning did not affect closed loop brain control in a virtual reality environment when preceded by a text-based decoder training paradigm.