论文标题
EEG序列中数据压缩的指纹
Fingerprints of data compression in EEG sequences
论文作者
论文摘要
经典的猜想是，大脑通过将概率模型分配给刺激序列来压缩数据。与此猜想相关的一个重要问题是大脑使用哪种模型来执行其压缩任务。我们通过引入新的统计模型选择程序来解决此问题，旨在研究大脑执行数据压缩的方式。我们的过程使用上下文树模型来表示刺激的序列和一种新的投射方法，用于聚集脑电图段。起点是一个实验协议，其中记录了脑电图数据，而参与者则暴露于由随机链产生的听觉刺激。使用两个不同的上下文树模型产生的刺激序列的仿真研究，该模型具有由两个不同算法产生的EEG段的元素，总结了本文。
It has been classically conjectured that the brain compresses data by assigning probabilistic models to sequences of stimuli. An important issue associated to this conjecture is what class of models is used by the brain to perform its compression task. We address this issue by introducing a new statistical model selection procedure aiming to study the manner by which the brain performs data compression. Our procedure uses context tree models to represent sequences of stimuli and a new projective method for clustering EEG segments. The starting point is an experimental protocol in which EEG data is recorded while a participant is exposed to auditory stimuli generated by a stochastic chain. A simulation study using sequences of stimuli generated by two different context tree models with EEG segments generated by two distinct algorithms concludes this article.
