Abstract：
Microstate analysis is a spatiotemporal method where instantaneous scalp potential topography represents the current state of the brain. The temporal evolution of these scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks. It has been proven potential in identification and characterization of neurophysiological indicators associated with neuropsychiatric conditions. Changes in microstates connected to symptoms and cognitive impairments of neuropsychiatric conditions. It is useful in the study of cognitive processes and disorders related to memory. Researchers may probe into the relationships between microstates and other cognitive processes, such as memory retrieval and encoding. This is a tool for clinicians to enhance the precision of diagnosis and inform possibilities for treatment by acquiring information regarding individual diversity in microstates could lead to tailored medical methods. Customizing treatment according to a patient\'s microstate patterns could improve the efficacy of treatment. The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry and neurological disorders. A section in the review article has been dedicated to source localization of EEG microstates. The selection of review papers shed light on the importance and huge potential of application of EEG microstate analysis in various neuropsychological processes. The review concludes with the need for standardization of microstate analysis. It suggests the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliability and acceptability of microstate analysis as reliable biomarkers for neurological conditions in the future.
摘要：
微状态分析是一种时空方法，其中瞬时头皮电位形貌表示大脑的当前状态。这些头皮形貌的时间演变可以理解远处电极之间的长程相干的准稳定周期，反映大规模皮层网络中的功能协调。已证明在鉴定和表征与神经精神状况相关的神经生理指标方面具有潜力。与神经精神疾病的症状和认知障碍相关的微观状态的变化。它可用于研究与记忆有关的认知过程和障碍。研究人员可能会探讨微观状态与其他认知过程之间的关系，如内存检索和编码。这是临床医生通过获取有关微状态中个体多样性的信息来提高诊断精度并告知治疗可能性的工具，这可能导致量身定制的医疗方法。根据患者的微状态模式定制治疗可以提高治疗效果。这篇综述的论文涵盖了广泛的领域，包括与记忆相关的疾病，精神病学和神经系统疾病。评论文章中的一部分专门介绍了EEG微状态的源定位。评论论文的选择揭示了EEG微状态分析在各种神经心理过程中应用的重要性和巨大潜力。该评论的结论是需要对微观状态分析进行标准化。它建议采用广泛接受的机器学习技术来提高准确性，微状态分析作为未来神经系统疾病可靠生物标志物的可靠性和可接受性。
