Inferring Brain Signals Synchronicity From a Sample of EEG Readings

ABSTRACT

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms 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 nontrivial 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. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.

KEYWORDS:

• Consensus clustering
• EEG
• Hierarchical mixture models
• Spectral clustering

Additional information

Funding

This work was supported by the grant R01 GM111378-01A1 (DS, DT, CS) from the National Institute of General Medical Sciences.