Decoding force from deep brain electrodes in Parkinsonian patients

Syed A Shah, Huiling Tan, Peter Brown

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

Abstract

Limitations of many Brain Machine Interface (BMI) systems using invasive electrodes include reliance on single neurons and decoding limited to kinematics only. This study investigates whether force-related information is present in the local field potential (LFP) recorded with deep brain electrodes using data from 14 patients with Parkinson's disease. A classifier based on logistic regression (LR) is developed to classify various force stages, using 10-fold cross validation. Least Absolute and Shrinkage Operator (Lasso) is then employed in order to identify the features with the most predictivity. The results show that force-related information is present in the LFP, and it is possible to distinguish between various force stages using certain frequency-domain (delta, beta, gamma) and time-domain (mobility) features in real-time.

Original languageEnglish
Title of host publication 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages5717-5720
Number of pages4
Volume2016
DOIs
Publication statusPublished - 18 Oct 2016

Publication series

NameConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1557-170X

Keywords

  • Biomechanical Phenomena
  • Brain/physiopathology
  • Brain-Computer Interfaces
  • Electrodes
  • Humans
  • Neurons/physiology
  • Parkinson Disease/physiopathology

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