Overcoming measurement time variability in brain machine interface.

  • Gowreesunker B
  • Tewfik A
  • Tadipatri V
 et al. 
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Abstract

We introduce a subspace learning approach for multi-channel Local
Field Potentials (LFP), and demonstrate its application in movement
direction decoding for 8 directions movement. We show that the subspace
learning method can effectively address the issue of signal instability
across recording sessions by extracting recurrent features from the
data. We present results for movement direction decoding, where we
trained on two recording sessions, and evaluated decoding performance
on a third session. We combine our method with a classifier based
on Error-Correcting Output Codes (ECOC) and Common Spatial Patterns
(CSP) and found improvement in Decoding Power (DP) from 76% to 88%
for a subject known to have strong inter-session variability. Furthermore,
we saw an increase from 86% to 90% DP with another subject which
exhibited significantly less variability.

Author-supplied keywords

  • Algorithms; Biomedical Engineering
  • Computer-Assisted; Time Factors
  • Neurological; Movement; Reproducibility of Result
  • methods; Brain
  • pathology; Equipment Design; Humans; Learning; Le

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Authors

  • B Vikrham Gowreesunker

  • Ahmed H Tewfik

  • Vijay A Tadipatri

  • Nuri F Ince

  • James Ashe

  • Giuseppe Pellizzer

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