Inferring Brain Signals Synchronicity From a Sample of EEG Readings

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.

从异质性脑电图（electroencephalogram，EEG）样本中推断同步脑活动模式，在科学与方法论层面均颇具挑战性。尽管依托多份个体脑电图读数以凸显脑激活的重复模式，在直觉与统计学层面均颇具吸引力，但跨被试整合信息仍存在非平凡的方法论难题。本文探讨了与同步神经元活动理解相关的若干科学问题，并提出了基于脑电图样本开展统计推断的方法论框架。本研究依托时序分析、聚类分析与函数型数据分析（functional data analysis）领域的经典研究成果，旨在借助现有成熟的分析技术重新阐释这一颇具挑战的推断问题。本文还对计算层面的相关问题予以关注，并提出了融合机器学习与贝叶斯（Bayesian）技术的解决方案。本文随附的代码已由副编辑（Associate Editor）进行可重复性（Reproducibility）审查，并可作为在线补充材料获取。