Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human’s physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain–computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.

从大脑获取的脑电图（EEG）信号可以有效地表示人类的生理和病理状态。迄今为止，人们已经开展了大量的工作来研究和分析脑电信号，旨在窥探复杂大脑系统的当前状态或演化特征。考虑到不同结构和功能的脑区域之间复杂的相互作用，脑网络受到了广泛的关注，并在脑机制研究方面取得了巨大进展。此外，深度学习以其自主、多层、多样化特征提取的特点，为解决包括脑状态研究在内的许多领域的复杂分类问题提供了有效可行的解决方案。两者在脑电信号分析方面都表现出了很强的能力，但是结合这两种理论来解决基于脑电信号的困难分类问题还处于起步阶段。我们回顾一下这两种理论在脑电信号研究中的应用，主要涉及脑机接口、神经障碍和认知分析。此外，我们还开发了一个结合递归图和卷积神经网络的框架来实现疲劳驾驶识别。结果表明，复杂网络和深度学习可以有效地实现功能互补，以实现更好的特征提取和分类，特别是在脑电信号分析中。