The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

中文翻译：

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使用静息态脑电图测量对乳酸水平进行分类

脑电图 (EEG) 信号已广泛用于研究大脑神经信息动态和行为以及使用机器和深度学习技术的影响。这项工作提出了一种基于快速傅里叶变换（FFT）的系统作为对人脑静息态脑电图（EEG）记录信号进行分类的特征提取方法。在所提出的系统中，FFT方法应用于静息态脑电图记录并计算相应的频带功率。提取的相对功率特征被提供给分类方法（分类器）作为分类目的的输入，通过预测乳酸酶水平高或低来衡量人类疲劳程度。为了验证所建​​议的方法，我们使用了一组精英级运动员记录的脑电图数据集，该运动员分为两类：不累，在进行剧烈运动之前的静息状态任务中记录脑电图信号；累了，在进行剧烈运动之前记录脑电图信号。进行剧烈运动后在静息状态下记录脑电图信号。三种不同分类器的性能通过两种性能指标（准确度和精度值）进行评估。K 最近邻 (KNN) 分类器的准确率达到 98% 以上。这项研究的结果表明，特征提取方案能够准确地对分析的脑电图信号进行分类，并预测乳酸酶水平的高低。许多研究领域，如物联网（IoT）和脑机接口（BCI），可以在许多关键的决策应用中利用所提出的系统的研究结果。