Inferring Brain Signals Synchronicity From a Sample of EEG Readings

Abstract

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.

Network Modeling in Biology: Statistical Methods for Gene and Brain Networks

“…They developed a generalized shrinkage estimator, a weighted average of a parametric and a nonparametric estimator, of the partial coherence matrix. Moreover, [122] employed time-series, clustering and functional data analysis to study spectral synchronicity and functional connectivity also using EEG data. [182] discussed using mutual information and partial mutual information to estimate functional connectivity network, and [26] further extended the method.…”