Title: Conex-Connect: Learning patterns in extremal brain connectivity from multi-channel EEG data
Authors: Matheus Guerrero - King Abdullah University of Science and Technology (Saudi Arabia) [presenting]
Raphael Huser - King Abdullah University of Science and Technology (Saudi Arabia)
Hernando Ombao - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia)
Abstract: Epilepsy is a chronic brain disease affecting more than 50 million people globally. An epileptic seizure occurs as an abnormal temporary shock to the neuronal system, with results varying from a very brief and almost imperceptible loss of consciousness to uncontrollable spasms. Epilepsy is frequently diagnosed with electroencephalograms (EEGs), and statistical methods are widely used to analyze EEG signals. We propose a new approach to characterize brain connectivity during an epileptic seizure. Our method models the conditional extremal dependence for brain connectivity (Conex-Connect). It is a pioneering method in linking the association between extreme values of higher oscillations at a reference channel with the other channels of the brain network. We applied our method to EEG data from a patient diagnosed with left temporal lobe epilepsy, revealing changes in the conditional extremal dependence of brain connectivity. Pre-seizure, the dependence is notably stable for all channels when conditioning on extreme values of the focal seizure area. Post-seizure, the dependence between channels is weaker, and dependence patterns are more ``chaotic''. Also, in terms of spectral decomposition, high values of the higher frequency band are the most relevant features to explain the conditional extremal dependence of brain connectivity.