Non-parametric directionality analysis: extension for removal of a single common predictor and application to time series - Halliday, David M.; Senik, Mohd Harizal; Stevenson, Carl W.; Mason, Robert (Elsevier, 2016)
BACKGROUND: The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity.