Continuous state-dependent decoders for brain machine interfaces

Christian Ethier*, Nicholas A. Sachs, Lee E. Miller

*Corresponding author for this work

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

4 Scopus citations

Abstract

One of the characteristics of cursor movement controlled via a brain machine interface is a trade-off between the ability to move rapidly between targets and the ability to hold the cursor steadily within a target. We propose to address this limitation by classifying independent movement and posture states, and using neural decoders with optimum dynamical properties for each state. This paper investigates two methods of classifying the state of a limb based on the offline analysis of neural discharge. We also tested the performance of state-dependent decoders that either apply additional smoothing during the posture state or consist of separate filters trained explicitly on data from the different movement states. This work suggests that a state-dependent decoder may provide significantly improved BMI performance.

Original languageEnglish (US)
Title of host publication2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
Pages473-477
Number of pages5
DOIs
StatePublished - Jul 20 2011
Event2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011 - Cancun, Mexico
Duration: Apr 27 2011May 1 2011

Publication series

Name2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011

Other

Other2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011
CountryMexico
CityCancun
Period4/27/115/1/11

ASJC Scopus subject areas

  • Neuroscience(all)

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