title: Statistically significant time-varying neural connectivity estimation using generalized partial directed coherence.
abstract: This paper illustrates the effectiveness of generalized partial directed coherence (gPDC) in characterizing time-varying neural connectivity by properly extrapolating its single trial asymptotic statistical results to a multi trial setting. Time-varying estimation is performed with a sliding-window procedure based on the proposal in [1], whereby a time-frequency map of the connectivity between channels is built. The technique is validated on a non-linear toy model generating simulated EEG and then applied to a publicly available real EEG dataset for benchmarking purposes.
authors.forename: Pedro L C
authors.initials: PL
authors.lastname: Rodrigues
authors.forename: Luiz A
authors.initials: LA
authors.lastname: Baccala
mesh_terms: D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000000981:diagnostic imaging / Q000502:physiology; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000502:physiology