title: Signal acquisition and analysis for cortical control of neuroprosthetics.
abstract: Work in cortically controlled neuroprosthetic systems has concentrated on decoding natural behaviors from neural activity, with the idea that if the behavior could be fully decoded it could be duplicated using an artificial system. Initial estimates from this approach suggested that a high-fidelity signal comprised of many hundreds of neurons would be required to control a neuroprosthetic system successfully. However, recent studies are showing hints that these systems can be controlled effectively using only a few tens of neurons. Attempting to decode the pre-existing relationship between neural activity and natural behavior is not nearly as important as choosing a decoding scheme that can be more readily deployed and trained to generate the desired actions of the artificial system. These artificial systems need not resemble or behave similarly to any natural biological system. Effective matching of discrete and continuous neural command signals to appropriately configured device functions will enable effective control of both natural and abstract artificial systems using compatible thought processes.
authors.affiliation: The Biodesign Institute & Harrington Department of Bioengineering, ECG 334, Arizona State University, Tempe, Arizona, 85287-9709, USA. Steve.HelmsTillery@asu.edu
authors.forename: Stephen I Helms
authors.initials: SI
authors.lastname: Tillery
authors.forename: Dawn M
authors.initials: DM
authors.lastname: Taylor
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D006801:Humans; D009044:Motor Cortex / Q000502:physiology*; D009046:Motor Neurons / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010243:Paralysis / Q000628:therapy*; D019736:Prostheses and Implants / Q000639:trends*; D011597:Psychomotor Performance / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface