Improved linear BMI systems via population averaging

Jack DiGiovanna, Justin C. Sanchez, Jose C. Principe

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

5 Scopus citations

Abstract

We investigate population averaging as a preprocessing stage for linear FIR BMIs. Population averaging is a biologically-inspired technique based on spatial constraints and neuronal correlation. We achieve a statistically significant improvement in accuracy while substantially (45%) reducing model parameters. Further analysis is performed to show that population averaging improves model accuracy by reducing variance in estimating the firing rate from spike bins. However, we find that population averaging provides a greater accuracy improvement than other groupings which also reduce firing rate variance. Our results suggest that appropriate spatial organization of neural signals enhances BMI performance.

Original languageEnglish (US)
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages1608-1611
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: Aug 30 2006Sep 3 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Other

Other28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period8/30/069/3/06

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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  • Cite this

    DiGiovanna, J., Sanchez, J. C., & Principe, J. C. (2006). Improved linear BMI systems via population averaging. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 (pp. 1608-1611). [4029847] (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings). https://doi.org/10.1109/IEMBS.2006.260496