An electroencephalogram (EEG) is a significant source of diagnosing brain issues. It is also a mediator between the external world and the brain, especially in the case of any mental illness; however, it has been widely used to monitor the dynamics of the brain in healthy subjects. This paper discusses the resting state of the brain with eyes open (EO) and eyes closed (EC) by using sixteen channels by the use of conventional frequency bands and entropy of the EEG signal. The Fast Fourier Transform (FFT) and sample entropy (SE) of each sensor are computed as methods of feature extraction. Six classifiers, including logistic regression (LR), K-Nearest Neighbors (KNN), linear discriminant (LD), decision tree (DT), support vector machine (SVM), and Gaussian Naive Bayes (GNB) are used to discriminate the resting states of the brain based on the extracted features. EEG data were epoched with one-second-length windows, and they were used to compute the features to classify EO and EC conditions. Results showed that the LR and SVM classifiers had the highest average classification accuracy (97%). Accuracies of LD, KNN, and DT were 95%, 93%, and 92%, respectively. GNB gained the least accuracy (86%) when conventional frequency bands were used. On the other hand, when SE was used, the average accuracies of SVM, LD, LR, GNB, KNN, and DT algorithms were 92% 90%, 89%, 89%, 86%, and 86%, respectively.

中文翻译：

---

基于样本熵和脑电图（EEG）功率谱的静息状态分类

脑电图 (EEG) 是诊断大脑问题的重要来源。它也是外部世界和大脑之间的中介，尤其是在患有任何精神疾病的情况下；然而，它已被广泛用于监测健康受试者的大脑动态。本文利用脑电图信号的常规频带和熵，利用十六个通道讨论了睁眼（EO）和闭眼（EC）时大脑的静息状态。每个传感器的快速傅立叶变换（FFT）和样本熵（SE）被计算作为特征提取的方法。六个分类器，包括逻辑回归（LR）、K最近邻（KNN）、线性判别（LD）、决策树（DT）、支持向量机（SVM）和高斯朴素贝叶斯（GNB）用于区分剩余的基于提取的特征的大脑状态。EEG 数据采用一秒长度的窗口，用于计算特征以对 EO 和 EC 条件进行分类。结果表明，LR 和 SVM 分类器的平均分类准确率最高（97%）。LD、KNN 和 DT 的准确率分别为 95%、93% 和 92%。当使用传统频段时，GNB 的准确度最低 (86%)。另一方面，当使用SE时，SVM、LD、LR、GNB、KNN和DT算法的平均准确度分别为92%、90%、89%、89%、86%和86%。