Decoding high level signals for asynchronous brain machine interfaces

Byron Olson, Jennie Si, Jason Silver

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

1 Scopus citations

Abstract

While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an 'always on' interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.

Original languageEnglish (US)
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages6569-6572
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

Fingerprint Dive into the research topics of 'Decoding high level signals for asynchronous brain machine interfaces'. Together they form a unique fingerprint.

Cite this