标题： 基于马尔可夫链模型的脑电图信号同步状态转换预测 显示英文标题

标题： Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models

摘要： 本文提出了一种随机模型，利用马尔可夫链的概念来描述在多通道脑电图（EEG）信号中发现的毫秒级有序准稳定相位同步模式或同步状态之间的状态转移。 从128通道EEG信号的100次试验中估计了一阶和二阶转移概率矩阵，用于马尔可夫链建模。 在基于数据划分的交叉验证方案下，也比较了此类有限马尔可夫链模型在同步状态转移中的预测准确率。 显示英文摘要

摘要： This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.