脑电图（EEG）是一个复杂的信号，可能需要几年的培训，先进的信号处理，和特征提取方法来正确解释。最近,许多方法已经被用来提取和分类脑电图数据。这项研究回顾了62篇论文，这些论文使用EEG信号来检测驾驶员的嗜睡，在2018年1月至2022年之间发布。我们从大量文献中提取趋势并突出有趣的方法，为未来的研究提供信息并制定建议。为了找到发表在科学期刊上的相关论文，会议,和电子预打印存储库，研究人员搜索了涵盖科学和工程领域的主要数据库。每次调查,关于(1)数据的许多数据项，(2)使用的频道，(3)提取和分类程序,(4)提取结果。然后对这些项目进行逐一分析，以发现趋势。我们的分析表明，研究中使用的EEG数据量各不相同。我们看到超过一半的研究使用模拟驾驶实验。大约21%的研究使用支持向量机(SVM)，而19%的人使用卷积神经网络(CNN)。总的来说,我们可以得出结论，困倦和疲劳会损害驾驶性能，导致司机更容易受到危险情况的影响。
Electroencephalography (EEG) is a complex signal that may require several years of training, advanced signal processing, and feature extraction methodologies to interpret correctly. Recently, many methods have been used to extract and classify EEG data. This study reviews 62 papers that used EEG signals to detect driver drowsiness, published between January 2018 and 2022. We extract trends and highlight interesting approaches from this large body of literature to inform future research and formulate recommendations. To find relevant papers published in scientific journals, conferences, and electronic preprint repositories, researchers searched major databases covering the domains of science and engineering. For each investigation, many data items about (1) the data, (2) the channels used, (3) the extraction and classification procedure, and (4) the outcomes were extracted. These items were then analyzed one by one to uncover trends. Our analysis reveals that the amount of EEG data used across studies varies. We saw that more than half the studies used simulation driving experimental. About 21% of the studies used support vector machine (SVM), while 19% used convolutional neural networks (CNN). Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.
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