基于功率谱熵特征提取的脑电波大脑年龄预测

 大脑会随着年龄的增加而出现功能衰退，通过决策实验获取年轻人和中老年人的脑电信号，可以定量分析大脑随年龄增长而出现的变化。提出了一种基于熵的脑电波刻画方法，并利用机器学习的方法能够比较准确地预测人的大脑年龄。研究表明，脑电波功率谱熵（PSE）具有良好的时域分辨能力和更准确的区分效果，年轻人在做决策时的脑电波功率谱熵的分布是大于中老年人的，即年轻人所产生的脑电波信息量更大。此外，支持向量机（SVM）的分类效果优于随机森林（RF）方法，最高平均精度达88.02%，比随机森林高出2.66%。通过基尼指数对特征重要性排序，还发现决策过程中左眼电区域、大脑的颞和中央区域的决策反应差异很大，分类器更容易在这些特征区域做出更好的分类。

The brain age prediction based on the power spectrum entropy feature extraction

The brain activity sees a functional decline in the aging process. The Electroencephalogram (EEG) signals of young and elderly people are obtained by the decision-making experiment to be used to quantitatively analyze the changes of the brain with age. This paper presents an entropy-based characterization method of the EEG, which can accurately predict the human brain age by the machine learning method. The results show that there is a rich performance with the power spectrum entropy (PSE) in the time-resolution ability and the effect of the accurate differentiation. The distribution of the entropy of the young people in a decision-making process has a greater intensity than that of the elderly. In other words, the amount of information of the brain generated by young people is larger than that of the elderly. In addition, the support vector machine (SVM) is superior to the random forest (RF) method, since the highest average accuracy (ACC) is 88.02% and is 2.66% higher than that of the RF method. It is also found that a great difference is observed in the responses of the decision-making, especially in the left EOG, temporal and central regions of the brain, which can be more easily classified by the classifiers.