Use of a Bayesian maximum-likelihood classifier to generate training data for brain–machine interfaces
- 8 June 2011
- journal article
- Published by IOP Publishing in Journal of Neural Engineering
- Vol. 8 (4), 046009
- https://doi.org/10.1088/1741-2560/8/4/046009
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.Keywords
This publication has 56 references indexed in Scilit:
- Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodesJournal of Neural Engineering, 2011
- Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode ArraysJournal of Neurophysiology, 2009
- Cyber-Workstation for Computational NeuroscienceFrontiers in Neuroengineering, 2009
- Direct control of paralysed muscles by cortical neuronsNature, 2008
- Minocycline increases quality and longevity of chronic neural recordingsJournal of Neural Engineering, 2007
- Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortexIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2005
- Inference of hand movements from local field potentials in monkey motor cortexNature Neuroscience, 2003
- Learning to Control a Brain–Machine Interface for Reaching and Grasping by PrimatesPLoS Biology, 2003
- Brain–computer interfaces for communication and controlClinical Neurophysiology, 2002
- Direct Cortical Control of 3D Neuroprosthetic DevicesScience, 2002