title: Use of a Bayesian maximum-likelihood classifier to generate training data for brain-machine interfaces.
abstract: Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.
authors.affiliation: Department of Biomedical Engineering, University of Michigan, 1101 Beal Ave, 2247 LBME, Ann Arbor, MI 48109, USA.
authors.forename: Kip A
authors.initials: KA
authors.lastname: Ludwig
authors.forename: Rachel M
authors.initials: RM
authors.lastname: Miriani
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Langhals
authors.forename: Timothy C
authors.initials: TC
authors.lastname: Marzullo
authors.forename: Daryl R
authors.initials: DR
authors.lastname: Kipke
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001522:Behavior, Animal / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D003216:Conditioning, Operant / Q000502:physiology; D004558:Electric Stimulation; D004567:Electrodes, Implanted; D004569:Electroencephalography; D055724:Electrophysiological Phenomena; D005072:Evoked Potentials, Auditory / Q000502:physiology; D016013:Likelihood Functions; D009474:Neurons / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology; D051381:Rats; D017207:Rats, Sprague-Dawley; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D014796:Visual Perception / Q000502:physiology