基于自适应模板法的脑电信号转移熵分析

脑电信号是一种产生机理相当复杂且非常微弱的随机信号, 综合反映了大脑组织的脑电活动及大脑的功能状态. 由于脑电信号的微弱性, 传统的基本模板方法在脑电信号分析上得到了良好的应用. 为进一步提升分析脑电信号的性能, 提出了一种新的基于自适应模板的转移熵方法并分析了青少年脑电与成年人脑电信号. 结果表明: 对于青少年脑电还是成年人脑电, 与基本模板法相比, 基于自适应模板法的转移熵可以更显著地表示脑电信号的耦合作用, 并且具有更好的区分度, 这将能更好地捕捉到信号中的动态信息、系统动力学复杂性的改变. 同时, 该方法将更有利于医学临床诊断的辅助检测, 对脑电信号是否处于病理状态的诊断提供了新的更好的判断依据.

Transfer entropy analysis of electroencephalogram based on adaptive template method

Electroencephalogram (EEG) is a very weak random signal with complex mechanism, which comprehensively reflects the activities and the functional states of brain tissue. Due to the weak characteristic of EEG, the traditional basic template method is a good tool for EEG analysis. In order to further enhance the performance of this method, we propose a new transfer entropy method based on adaptive template. The method improves the symbolization of time series based on the original basic template method. Numerical experiments show that the improved adaptive template method can obtain better dynamic characteristics, and also has better ability to distinguish the results in the analysis of time series. We use the transfer-entropy-based adaptive template method to analyze adolescent and adult EEG. We also study the relationship of the transfer-entropy-based adaptive template method to the total data length L and the data length l of the divided cells. Numerical results show that the transfer entropy value of adult EEG based on adaptive template is significantly higher than that of teenager EEG. This indicates that adult has a more significantly mental activity and the functional status of the brain is more complex. We then apply this method to human EEG signals and investigate their statistical properties. The results show that compared with the result of the basic method, the transfer-entropy-based adaptive template method can significantly show the EEG coupling for adolescents and adults EEG, which has a better discrimination and can better capture dynamic information and the change of the system dynamic complexity. At the same time, it will be more conducive to clinical diagnosis and provides a new and better method to judge whether brain is in a pathological state.