Decoding hand trajectories from ECoG recordings via kernel least-mean-square algorithm

Aysegul Gunduz, Jung Phil Kwon, Justin C. Sanchez, Jose C. Principe

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

7 Scopus citations

Abstract

Prediction of two dimensional hand trajectories from cortical surface recordings entails finding a functional mapping from spectral modulations in multidimensional channels to instantaneous hand positions. Such studies thus far have been conducted through linear adaptive filters, even though, the functional mapping from the cortical activity to behavior might be nonlinear. Herein, we employ a nonlinear adaptive filter, kernel least mean square (KLMS), which nonlinearly map inputs to a higher dimensional feature space in which inner products can be efficiently computed. The methodology is a simple and effective nonlinear extension of the least mean square (LMS) algorithm. Preliminary results show significant improvements in mean squared error (MSE) values of reconstructed trajectories compared to linear methods (LMS) at a confidence level of 95% in the axis of highest excursion.

Original languageEnglish (US)
Title of host publication2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Pages267-270
Number of pages4
DOIs
StatePublished - Oct 27 2009
Event2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 - Antalya, Turkey
Duration: Apr 29 2009May 2 2009

Publication series

Name2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09

Other

Other2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09
Country/TerritoryTurkey
CityAntalya
Period4/29/095/2/09

ASJC Scopus subject areas

  • Biomedical Engineering
  • Clinical Neurology
  • Neuroscience(all)

Fingerprint

Dive into the research topics of 'Decoding hand trajectories from ECoG recordings via kernel least-mean-square algorithm'. Together they form a unique fingerprint.

Cite this