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 con

SUBMITTER: Ludwig KA 

PROVIDER: S-EPMC3145156 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature