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UID:2445@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180903T143000
DTEND;TZID=Europe/Paris:20180903T153000
DTSTAMP:20180819T123000Z
URL:https://www.i2m.univ-amu.fr/evenements/spectral-causality-in-multivari
 ate-signals-beyond-linearity/
SUMMARY: (...): Spectral causality in multivariate signals: beyond linearit
 y
DESCRIPTION:: There has been a strong interest in causality testing in brai
 n signals. Most of the current methods are based on vector autoregressive 
 models and the limitations include potential model misspecification and th
 e ability to capture only linear associations. Our proposed approach will 
 be to study causality via oscillatory activities (dependence between diffe
 rent frequency bands). Of prime interest here is the notion of ``spectral 
 causality” which is broadly characterized as the extent to which an osci
 llatory activity in a population of neurons can predict various oscillator
 y activities in another region at a future time point. Using these oscilla
 tions\, we will build a class of functional multivariate cross-frequency o
 scillatory models so that our method can capture potential non-linear depe
 ndence of the present and past oscillatory activity. The new framework wil
 l be illustrated to multichannel electroencephalograms (EEG) recorded in a
 n auditory study with the goal of differentiating the causality structure 
 between the healthy and schizophrenic groups and to study the impact of tr
 anscranial magnetic stimulation (TMS) on modifying or moderating causality
  between regions.https://www.kaust.edu.sa/en/study/faculty/hernando-ombao
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DTSTART:20180325T030000
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