Clustering Brain Extreme Communities from Multi-Channel EEG Data

• Activity Number: 26 - Imaging Speed Session
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Matheus Bartolo Guerrero* and Raphaël Huser and Hernando Ombao
• Companies: KAUST and KAUST and King Abdullah University of Science and Technology
• Keywords: Epilepsy; Extreme-value theory; Spectral measure; Clustering techniques; Non-stationary time series

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 cluster methods for characterizing brain connectivity rely on the bulk of EEG distribution, such as coherence. Here, we use a spherical k-means procedure based on extreme amplitudes of EEG signals during an epileptic seizure. With this approach, cluster centers can be interpreted as "extremal prototypes," revealing the dependence structures of EEG channel communities. The way cluster components relate to each other can be used as an exploratory tool to classify EEG channels into asymptotic independents or asymptotic dependents. We illustrate the use of this approach by an application to a real dataset from a patient with left temporal lobe epilepsy.