Learning mappings in brain machine interfaces with echo state networks

Yadunandana N. Rao, Sung Phil Kim, Justin C. Sanchez, Deniz Erdogmus, Jose C. Principe, Jose M. Carmena, Mikhail A. Lebedev, Miguel A. Nicolelis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Brain Machine Interfaces (BMI) utilize linear or non-linear models to map the neural activity to the associated behavior which is typically the 2-D or 3-D hand position of a primate. Linear models are plagued by the massive disparity of the input and output dimensions thereby leading to poor generalization. A solution would be to use non-linear models like the Recurrent Multi-Layer Perceptron (RMLP) that provide parsimonious mapping functions with better generalization. However, this results in a drastic increase in the training complexity, which can be critical for practical use of a BMI. This paper bridges the gap between superior performance per trained weight and model learning complexity. Towards this end, we propose to use Echo State Networks (ESN) to transform the neuronal firing activity into a higher dimensional space and then derive an optimal sparse linear mapping in the transformed space to match the hand position. The sparse mapping is obtained using a weight constrained cost function whose optimal solution is determined using a stochastic gradient algorithm.

Original languageEnglish (US)
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesV233-V236
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeV
ISSN (Print)1520-6149

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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