论文标题
从脑电图数据中检索听觉刺激的概率序列的结构
Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data
论文作者
论文摘要
使用一种新的概率方法，我们对随机链产生的听觉刺激序列与获得的脑电图（EEG）数据进行建模，而199名参与者则暴露于这些刺激。产生刺激的链的结构的特征是生根和标记的树，其叶子（以后称为上下文）代表了过去的刺激的序列，该序列统治了下一个刺激的选择。经典的猜想声称大脑将概率模型分配给刺激样本。如果这是正确的，则应在大脑活动中编码生成刺激序列的上下文树。使用创新的统计程序，我们表明可以有效地从脑电图数据中提取这种上下文树，从而支持经典的猜想。
Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.
