Time-varying MVAR algorithms for directed connectivity analysis: Critical comparison in simulations and benchmark EEG data.

ABSTRACT: Human brain function depends on directed interactions between multiple areas that evolve in the subsecond range. Time-varying multivariate autoregressive (tvMVAR) modeling has been proposed as a way to help quantify directed functional connectivity strengths with high temporal resolution. While several tvMVAR approaches are currently available, there is a lack of unbiased systematic comparative analyses of their performance and of their sensitivity to parameter choices. Here, we critically compare four recursive tvMVAR algorithms and assess their performance while systematically varying adaptation coefficients, model order, and signal sampling rate. We also compared two ways of exploiting repeated observations: single-trial modeling followed by averaging, and multi-trial modeling where one

SUBMITTER: Pagnotta MF 

PROVIDER: S-EPMC5995381 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature