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    Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data

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    Type
    Preprint
    Authors
    Guerrero, Matheus B.
    Huser, Raphaël cc
    Ombao, Hernando cc
    KAUST Department
    King Abdullah University of Science and Technology (KAUST) Statistics Program, CEMSE Divison.
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021-01-03
    Permanent link to this record
    http://hdl.handle.net/10754/667223
    
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    Abstract
    Epilepsy is a chronic neurological disorder affecting more than 50 million people globally. An epileptic seizure acts like a temporary shock to the neuronal system, disrupting normal electrical activity in the brain. Epilepsy is frequently diagnosed with electroencephalograms (EEGs). Current methods study the time-varying spectra and coherence but do not directly model changes in extreme behavior. Thus, we propose a new approach to characterize brain connectivity based on the joint tail behavior of the EEGs. Our proposed method, the conditional extremal dependence for brain connectivity (Conex-Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels. Using the Conex-Connect method, we discover changes in the extremal dependence driven by the activity at the foci of the epileptic seizure. Our model-based approach reveals that, pre-seizure, the dependence is notably stable for all channels when conditioning on extreme values of the focal seizure area. Post-seizure, by contrast, the dependence between channels is weaker, and dependence patterns are more "chaotic". Moreover, in terms of spectral decomposition, we find that high values of the high-frequency Gamma-band are the most relevant features to explain the conditional extremal dependence of brain connectivity.
    Publisher
    arXiv
    arXiv
    2101.09352
    Additional Links
    https://arxiv.org/pdf/2101.09352
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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