自闭症谱系障碍（ASD）是一种神经发育障碍，其特征是社交沟通困难以及重复和刻板行为。根据世界卫生组织的数据，全世界大约有百分之一的儿童患有自闭症。随着自闭症谱系障碍在全球的流行，及时、准确的诊断对于提高自闭症谱系障碍儿童的干预效果至关重要。传统的ASD诊断方法依赖于临床观察和行为评估，存在耗时且缺乏客观生物学指标的缺点。因此，基于机器学习和深度学习技术的自动化诊断方法应运而生，并变得非常重要，因为它们可以实现更加客观、高效、准确的ASD诊断。脑电图（EEG）是一种记录大脑自发电位活动变化的电生理监测方法，对于识别自闭症儿童具有重要意义。通过分析脑电图数据，可以检测自闭症儿童的异常同步神经元活动。本文全面回顾了使用传统机器学习方法和深度学习方法进行基于脑电图的 ASD 识别，包括它们的优点和潜在缺陷。此外，它还强调了寻找更有效和高效的方法来根据脑电图信号自动诊断自闭症所面临的挑战和机遇，旨在促进自闭症谱系障碍的自动识别。

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.