Statistics of Extremes for Neuroscience

2024/04/14 by Paolo Victor Redondo, Redondo, Paolo V., Matheus B. Guerrero +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Computer and information sciences #Fractal and DNA sequence analysis #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2404.09157

openalex publication_date 2024/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This chapter illustrates how tools from univariate and multivariate statistics of extremes can complement classical methods used to study brain signals and enhance the understanding of brain activity and connectivity during specific cognitive tasks or abnormal episodes, such as an epileptic seizure.

Cited by

• Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
• MOPED: A moving sum method for change point detection in pairwise extremal dependence

Related

• Statistical Challenges in Modeling Big Brain Signals
• Canonical Tail Dependence for Soft Extremal Clustering of Multichannel Brain Signals
• Proceedings of the 5th Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI) at NIPS 2015
• Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
• The Future of Data Analysis in the Neurosciences