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    SCAU: Modeling spectral causality for multivariate time series with applications to electroencephalograms

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    Type
    Preprint
    Authors
    Pinto-Orellana, Marco Antonio
    Mirtaheri, Peyman
    Hammer, Hugo L.
    Ombao, Hernando cc
    KAUST Department
    Biostatistics Group
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2021-05-13
    Permanent link to this record
    http://hdl.handle.net/10754/669256
    
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    Abstract
    Electroencephalograms (EEG) are noninvasive measurement signals of electrical neuronal activity in the brain. One of the current major statistical challenges is formally measuring functional dependency between those complex signals. This paper, proposes the spectral causality model (SCAU), a robust linear model, under a causality paradigm, to reflect inter- and intra-frequency modulation effects that cannot be identifiable using other methods. SCAU inference is conducted with three main steps: (a) signal decomposition into frequency bins, (b) intermediate spectral band mapping, and (c) dependency modeling through frequency-specific autoregressive models (VAR). We apply SCAU to study complex dependencies during visual and lexical fluency tasks (word generation and visual fixation) in 26 participants' EEGs. We compared the connectivity networks estimated using SCAU with respect to a VAR model. SCAU networks show a clear contrast for both stimuli while the magnitude links also denoted a low variance in comparison with the VAR networks. Furthermore, SCAU dependency connections not only were consistent with findings in the neuroscience literature, but it also provided further evidence on the directionality of the spatio-spectral dependencies such as the delta-originated and theta-induced links in the fronto-temporal brain network.
    Publisher
    arXiv
    arXiv
    2105.06418
    Additional Links
    https://arxiv.org/pdf/2105.06418.pdf
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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