Efficient temporal decomposition of local field potentials

Austin J. Brockmeier, José C. Príncipe, Babak Mahmoudi, Justin C. Sanchez

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

1 Scopus citations

Abstract

Local field potentials (LFPs) arise from dendritic currents that are summed by the brain tissue's impedance. By assuming that the rhythms existing in the LFPs result from the coordinated neural activity of sparse and transient neural assemblies transformed by the neural tissue, we propose to recover these neural assemblies sources using an independent component analysis on segments of a single LFP channel. The corresponding source signals and the set of temporal filters that operate on them constitute an efficient time-frequency decomposition of the LFP. This decomposition has the potential to identify sources that are more statistically dependent with stimuli or single-cell activity than the raw signal. In this work we show preliminary results on a synthetic dataset and a real dataset recorded from a rats nucleus accumbens during a reward administering experiment. When compared with the standard time-frequency analysis, this computational model for LFP analysis is totally data-driven because the filters, which form the basis for the decomposition, are estimated directly from the data.

Original languageEnglish (US)
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
StatePublished - Dec 5 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period9/18/119/21/11

Keywords

  • ICA
  • local field potential
  • multiscale neural signal analysis
  • statistical decoding

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Fingerprint Dive into the research topics of 'Efficient temporal decomposition of local field potentials'. Together they form a unique fingerprint.

  • Cite this

    Brockmeier, A. J., Príncipe, J. C., Mahmoudi, B., & Sanchez, J. C. (2011). Efficient temporal decomposition of local field potentials. In 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011 [6064598] (IEEE International Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2011.6064598