6533b838fe1ef96bd12a488a

RESEARCH PRODUCT

Robust estimation of partial directed coherence by the vector optimal parameter search algorithm

Silvia ErlaLuca FaesGiandomenico Nollo

subject

Mathematical optimizationMultivariate statisticsNeuroscience (all)Parameter search algorithmComputer scienceEstimation theoryMonte Carlo methodSystem identificationPartial directed coherenceBiomedical EngineeringAC powerAutoregressive modelSearch algorithmVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCoherence (signal processing)Brain connectivityNeurology (clinical)Algorithm

description

We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.

yearjournalcountryeditionlanguage
2009-04-01
10.1109/ner.2009.5109401http://hdl.handle.net/10447/278480