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 contributionpeer-review

25 Citations (Scopus)


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
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.
ISBN (Print)0780388747, 9780780388741
Publication statusPublished - 2005
Externally publishedYes
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 18 Mar 200523 Mar 2005

Publication series

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


Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA


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