Principle component feature detector for motor cortical control

J. Hu, Jennie Si, B. P. Olson, J. He

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

6 Scopus citations

Abstract

Principle component analysis (PCA) was performed on recorded neuronal action potentials from neural ensembles in rat's motor cortex when the rat was involved in a closed-loop real-time brain machine interface (BCI). The implanted rat was placed in a conditioning chamber, but freely moving, to decide which one of the two paddles should be activated to shift the light to the center. It is found that the principle component feature vectors revealed the importance of individual neurons and their temporal dynamics in relation to the intention of activating either left or right paddle. In addition, the first principle component feature has much higher discriminative capability than others although it represents only a few percentage of the total variance. Using the first principle component with the Bayes classifier achieved 90% classification accuracy, which is comparable with the accuracy obtained by a more sophisticated high performance support vector classifiers.

Original languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages4021-4024
Number of pages4
Volume26 VI
StatePublished - 2004
EventConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States
Duration: Sep 1 2004Sep 5 2004

Other

OtherConference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004
CountryUnited States
CitySan Francisco, CA
Period9/1/049/5/04

Keywords

  • Brain machine interface
  • Feature detection
  • Motor cortical control
  • Principle component analysis (PCA)
  • Support vector machines (SVM)

ASJC Scopus subject areas

  • Bioengineering

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  • Cite this

    Hu, J., Si, J., Olson, B. P., & He, J. (2004). Principle component feature detector for motor cortical control. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (Vol. 26 VI, pp. 4021-4024)