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.

brain age  /  decision-making  /  power spectrum entropy  /  support vector machine