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 / Description of output

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 / Materials (for Non-textual outputs)

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

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