Classification of Local Field Potentials using Gaussian Sequence Model

Taposh Banerjee, John Choi, Bijan Pesaran, Demba Ba, Vahid Tarokh

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

Abstract

A problem of classification of local field potentials (LFPs), recorded from the prefrontal cortex of a macaque monkey, is considered. An adult macaque monkey is trained to perform a memory based saccade. The objective is to decode the eye movement goals from the LFP collected during a memory period. The LFP classification problem is modeled as that of classification of smooth functions embedded in Gaussian noise. It is then argued that using minimax function estimators as features would lead to consistent LFP classifiers. The theory of Gaussian sequence models allows us to represent minimax estimators as finite dimensional objects. The LFP classifier resulting from this mathematical endeavor is a spectrum based technique, where Fourier series coefficients of the LFP data, followed by appropriate shrinkage and thresholding, are used as features in a linear discriminant classifier. The classifier is then applied to the LFP data to achieve high decoding accuracy. The function classification approach taken in the paper also provides a systematic justification for using Fourier series, with shrinkage and thresholding, as features for the problem, as opposed to using the power spectrum. It also suggests that phase information is crucial to the decision making.

Original languageEnglish (US)
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages218-222
Number of pages5
ISBN (Print)9781538615706
DOIs
StatePublished - Aug 29 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: Jun 10 2018Jun 13 2018

Publication series

Name2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Other

Other20th IEEE Statistical Signal Processing Workshop, SSP 2018
CountryGermany
CityFreiburg im Breisgau
Period6/10/186/13/18

Keywords

  • Block-wise James-Stein estimator
  • Gaussian sequence model
  • brain machine interface (BMI
  • minimax function estimators
  • pinsker's theorem

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Computer Networks and Communications

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  • Cite this

    Banerjee, T., Choi, J., Pesaran, B., Ba, D., & Tarokh, V. (2018). Classification of Local Field Potentials using Gaussian Sequence Model. In 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 (pp. 218-222). [8450778] (2018 IEEE Statistical Signal Processing Workshop, SSP 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSP.2018.8450778