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dc.contributor.authorGuerrero, Matheus B.
dc.contributor.authorHuser, Raphaël
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2021-02-04T11:31:54Z
dc.date.available2021-02-04T11:31:54Z
dc.date.issued2021-01-03
dc.identifier.urihttp://hdl.handle.net/10754/667223
dc.description.abstractEpilepsy 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.09352
dc.rightsArchived with thanks to arXiv
dc.titleConex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
dc.typePreprint
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST) Statistics Program, CEMSE Divison.
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.identifier.arxivid2101.09352
kaust.personGuerrero, Matheus B.
kaust.personHuser, Raphaël
kaust.personOmbao, Hernando
refterms.dateFOA2021-02-04T11:32:35Z


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