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arXiv · 2101.09352

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

Matheus B. Guerrero·Raphaël Huser·Hernando Ombao
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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.

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BibTeXRIS

Matheus B. Guerrero, Raphaël Huser, Hernando Ombao. 2021-01-03. Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data. https://arxiv.org/abs/2101.09352

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