Echo state networks for motor control of human ECoG neuroprosthetics

Aysegul Gunduz, Mustafa C. Ozturk, Justin C. Sanchez, Jose C. Principe

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

6 Scopus citations

Abstract

Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been previously shown to modulate with hand behavior by means of linear filters, though, with limited accuracy. We improve the online decoding performance of the amplitude modulation across the recording spectra by employing echo state networks (ESNs) which provide nonlinear mappings without compromising training complexity and filter order compared to basic linear filters and other neural networks. Preliminary results show an increase of 15% in the average correlation of ESN outputs with actual hand trajectories compared to linear mappings.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages514-517
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

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  • Cite this

    Gunduz, A., Ozturk, M. C., Sanchez, J. C., & Principe, J. C. (2007). Echo state networks for motor control of human ECoG neuroprosthetics. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 514-517). [4227327] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369722