Inferring the stability of LIFE through Brain Machine Interfaces

Jack Digiovanna; Luca Citi; Ken Yoshida; Jacopo Carpaneto; Jose C Principe; Justin C Sanchez; Silvestro Micera

doi:10.1109/IEMBS.2008.4649584

Inferring the stability of LIFE through Brain Machine Interfaces

Annu Int Conf IEEE Eng Med Biol Soc. 2008:2008:2008-11. doi: 10.1109/IEMBS.2008.4649584.

Authors

Jack Digiovanna¹, Luca Citi, Ken Yoshida, Jacopo Carpaneto, Jose C Principe, Justin C Sanchez, Silvestro Micera

Affiliation

¹ Dept. of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA. [email protected]

PMID: 19163087
DOI: 10.1109/IEMBS.2008.4649584

Abstract

We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting is applied to discriminate single units. We investigate the effect of discriminating between identified units on Brain Machine Interface (BMI) decoding performance. We infer the stability of LIFE based on decoding performance with and without current BMI methods to counter-act electrode neural signal degradation.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Action Potentials / physiology*
Algorithms
Animals
Electrodes, Implanted
Electrophysiology / methods
Female
Models, Neurological
Muscle, Skeletal / innervation*
Rabbits
Signal Processing, Computer-Assisted / instrumentation*
User-Computer Interface