Variable selection for motor cortical control of directions

Jing Hu, Jennie Si, Byron P. Olson, Jiping He

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

Abstract

In our previous work, a non-stereotypical brain machine interface system was implemented with freely-moving rats, and a nonlinear support vector machine (SVM) classifier was used to map neural signals in the rats' motor cortices onto a set of discrete classes of directions (Left and Right). In this paper, we provide a comprehensive analysis about the selection of neurons and temporal parameters, which is critical to the success of the system. We also show that pre-processing by principal component analysis (PCA) can reduce dimensions and improve accuracy.

Original languageEnglish (US)
Title of host publication2nd International IEEE EMBS Conference on Neural Engineering
Pages78-81
Number of pages4
Volume2005
DOIs
StatePublished - 2005
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States
Duration: Mar 16 2005Mar 19 2005

Other

Other2nd International IEEE EMBS Conference on Neural Engineering, 2005
CountryUnited States
CityArlington, VA
Period3/16/053/19/05

Keywords

  • Brain-machine interface
  • Motor cortical control
  • PCA
  • SVM
  • Variable selection

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Hu, J., Si, J., Olson, B. P., & He, J. (2005). Variable selection for motor cortical control of directions. In 2nd International IEEE EMBS Conference on Neural Engineering (Vol. 2005, pp. 78-81). [1419557] https://doi.org/10.1109/CNE.2005.1419557