Pontine nucleus audio stimuli detection & modeling for brain machine interface rehabilitation of conditional learning

Hanan Shteingart*, Aryeh Taub, Hagit Messer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to establish a brain-machine-interface (BMI) system that rehabilitates damaged cerebellum function of discrete motor learning, the detection of conditional and unconditional stimuli (CS and US) onset times based on electro-physiology recordings analysis is necessary. These signals are relayed through brainstem areas called Pontine Nucleus (PN) and the Inferior Olive (IO) respectively. In this paper we focus on the model based analysis of the PN and compare the expected model performance with the observed one with real samples. We suggest a model of multi-unit (MU) activity as filtered inhomogeneous Poisson pulses of evoked activity contaminated by homogenous spontaneous activity and thermal noise (Filtered Poisson-Poisson-Gaussian model). By assigning the likelihood into the generalize log likelihood test (GLRT), we show that the best expected feature is energy detection. The model parameters were estimated based on the recorded peri-stimuli-time-histogram (PSTH) by chi-square goodness-of-fit minimization. Monte Carlo simulation showed that the thermal noise can be neglected in respect to the spontaneous activity and also predicted the order of the observed empiric detection performance in terms of detection probability and area under the receiver operation characteristic (ROC) curve (AUC).

Original languageEnglish
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages21-24
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: 31 Aug 20093 Sep 2009

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Country/TerritoryUnited Kingdom
CityCardiff
Period31/08/093/09/09

Keywords

  • AUC
  • Brain machine interface
  • Brainstem
  • Classical conditioning
  • Detection
  • Electrophysiology
  • GLRT
  • Goodness-of-fit
  • Multi unit
  • Neural decoding
  • Poisson model
  • ROC
  • SVM
  • Simplex

Fingerprint

Dive into the research topics of 'Pontine nucleus audio stimuli detection & modeling for brain machine interface rehabilitation of conditional learning'. Together they form a unique fingerprint.

Cite this