Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time–frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

中文翻译：

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脑电图分析方法在神经科学和临床神经病学中的应用原理

脑电图（EEG）是一种无创测量大脑活动的方法。脑电图由于其安全性、高分辨率以及对大脑神经信号动态变化的超敏性，引起了科学研究和医学领域的极大兴趣。本文综述了脑电信号的类型、多种脑电信号分析方法以及相关方法在神经科学领域和诊断神经系统疾病中的应用。首先，介绍三种类型的脑电信号，包括时不变脑电图、精确事件相关脑电图和随机事件相关脑电图。其次，主要介绍了脑电分析方法的五个主要方向，包括功率谱分析、时频分析、连通性分析、源定位方法和机器学习方法，以及不同的子方法和效果评估为了解决同样的问题。最后强调了不同脑电分析方法的应用场景，并区分了同类方法的优缺点。本文旨在帮助研究者根据研究目标选择合适的脑电图分析方法，为后续研究提供参考，并总结当前存在的问题和对未来的展望。