Li Qian, Şentürk Damla, Sugar Catherine A, Jeste Shafali, DiStefano Charlotte, Frohlich Joel, Telesca Donatello
Department of Biostatistics, University of California, Los Angeles.
Department of Statistics, University of California, Los Angeles.
J Am Stat Assoc. 2019;114(527):991-1001. doi: 10.1080/01621459.2018.1518233. Epub 2019 Feb 27.
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.
从脑电图（EEG）的异质样本中推断同步脑活动模式在科学和方法上都具有挑战性。虽然依靠多个个体的读数来突出大脑激活的反复出现模式在直觉上和统计上很有吸引力，但跨受试者汇总信息存在一些重要的方法问题。我们讨论了与理解同步神经元活动相关的一些科学问题，并提出了一个从EEG读数样本进行统计推断的方法框架。我们的工作建立在时间序列、聚类和功能数据分析的经典贡献之上，试图在熟悉的分析技术背景下重新构建一个具有挑战性的推断问题。我们还关注了计算问题，并提出了一种基于机器学习和贝叶斯技术相结合的方案。
