Skip to Main content Skip to Navigation

Nonlinear models for neurophysiological time series

Abstract : In neurophysiological time series, strong neural oscillations are observed in the mammalian brain, and the natural processing tools are thus centered on narrow-band linear filtering.As this approach is too reductive, we propose new methods to represent these signals.We first focus on the study of phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency band, time-locked with a specific phase of a slow neural oscillation.We propose to use driven autoregressive models (DAR), to capture PAC in a probabilistic model. Giving a proper model to the signal enables model selection by using the likelihood of the model, which constitutes a major improvement in PAC estimation.%We first present different parametrization of DAR models, with fast inference algorithms and stability discussions.Then, we present how to use DAR models for PAC analysis, demonstrating the advantage of the model-based approach on three empirical datasets.Then, we explore different extensions to DAR models, estimating the driving signal from the data, PAC in multivariate signals, or spectro-temporal receptive fields.Finally, we also propose to adapt convolutional sparse coding (CSC) models for neurophysiological time-series, extending them to heavy-tail noise distribution and multivariate decompositions. We develop efficient inference algorithms for each formulation, and show that we obtain rich unsupervised signal representations.
Complete list of metadata

Cited literature [234 references]  Display  Hide  Download
Contributor : ABES STAR :  Contact
Submitted on : Wednesday, January 23, 2019 - 1:24:22 PM
Last modification on : Saturday, July 2, 2022 - 3:52:08 AM
Long-term archiving on: : Wednesday, April 24, 2019 - 1:46:06 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01990746, version 1


Tom Dupré La Tour. Nonlinear models for neurophysiological time series. Signal and Image Processing. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLT018⟩. ⟨tel-01990746⟩



Record views


Files downloads