A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

Journal Article (Journal Article)

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

Full Text

Duke Authors

Cited Authors

  • Kim, S-P; Sanchez, JC; Rao, YN; Erdogmus, D; Carmena, JM; Lebedev, MA; Nicolelis, MAL; Principe, JC

Published Date

  • June 2006

Published In

Volume / Issue

  • 3 / 2

Start / End Page

  • 145 - 161

PubMed ID

  • 16705271

International Standard Serial Number (ISSN)

  • 1741-2560

Digital Object Identifier (DOI)

  • 10.1088/1741-2560/3/2/009


  • eng

Conference Location

  • England