School of Social Sciences, Nanyang Technological University, Singapore.
Centre for Research and Development in Learning, Nanyang Technological University, Singapore.
Eur J Neurosci. 2022 Oct;56(7):5047-5069. doi: 10.1111/ejn.15800. Epub 2022 Sep 2.
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
越来越多的人开始使用复杂性分析来量化脑电图（EEG）信号所测量的神经活动。除了揭示线性方法可能无法揭示的大脑复杂神经元过程外，EEG 复杂性测量还显示出它们作为抑郁症和精神分裂症等精神病理学生物标志物的潜力。不幸的是，算法和源自数学概念的描述的不透明性使得理解复杂性是什么以及在心理学和神经精神病学研究中应用时如何得出一致的结论变得困难。在这篇综述中，我们提供了现有 EEG 复杂性测量方法的概述和入门级解释，这些方法可大致分为可预测性和规律性的度量。然后，我们综合了心理学科学不同领域的复杂性研究结果，即在意识研究、情绪和焦虑障碍、精神分裂症、神经发育和神经退行性疾病以及整个生命周期的变化方面，同时解决了数据差异背后的一些理论和方法问题。最后，我们介绍了在选择和解释这些指标时需要考虑的重要因素。
