Hernando OMBAO – Spectral causality in multivariate signals : beyond linearity

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Date(s) - 3 septembre 2018

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There has been a strong interest in causality testing in brain signals. Most of the current methods are based on vector autoregressive models and the limitations include potential model misspecification and the ability to capture only linear associations. Our proposed approach will be to study causality via oscillatory activities (dependence between different frequency bands). Of prime interest here is the notion of “spectral causality” which is broadly characterized as the extent to which an oscillatory activity in a population of neurons can predict various oscillatory activities in another region at a future time point. Using these oscillations, we will build a class of functional multivariate cross-frequency oscillatory models so that our method can capture potential non-linear dependence of the present and past oscillatory activity. The new framework will be illustrated to multichannel electroencephalograms (EEG) recorded in an auditory study with the goal of differentiating the causality structure between the healthy and schizophrenic groups and to study the impact of transcranial magnetic stimulation (TMS) on modifying or moderating causality between regions. https://www.kaust.edu.sa/en/study/faculty/hernando-ombao Hernando OMBAO [