Sun Congzhong, Mou Chaozhou
School of Mathematics and Statistics, Shandong University, Weihai, China.
Front Neurosci. 2023 Jul 13;17:1203059. doi: 10.3389/fnins.2023.1203059. eCollection 2023.
Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.
由于其便携性和简易性，脑电图（EEG）在脑机接口（BCI）系统中变得越来越重要。在本文中，我们全面回顾了自2021年以来脑电图信号处理技术的研究，重点关注预处理、特征提取和分类方法。我们分析了从学术搜索引擎（包括中国知网、PubMed、《自然》、IEEE Xplore和ScienceDirect）检索到的61篇研究文章。对于预处理，我们重点关注创新提出的预处理方法、通道选择和数据增强。数据增强分为传统方法（滑动窗口、分割与重组以及噪声注入）和深度学习方法[生成对抗网络（GAN）和变分自编码器（VAE）]。我们还关注深度学习的应用以及多方法融合方法，包括传统算法融合以及传统算法与深度学习之间的融合。我们的分析分别在预处理、特征提取和分类方向上识别出35项（57.4%）、18项（29.5%）和37项（60.7%）研究。我们发现预处理方法已在脑电图分类中广泛应用（96.7%的综述论文），并且一些研究进行了对比实验以验证预处理。我们还讨论了通道选择和数据增强的采用情况，并总结了关于数据增强的几个值得一提的问题。此外，深度学习方法在脑电图分类中显示出巨大潜力，卷积神经网络（CNN）是深度神经网络的主要结构（92.3%的深度学习论文）。我们总结并分析了几种创新神经网络，包括CNN和多结构融合。然而，我们也识别出当前深度学习技术在脑电图分类中的几个问题和局限性，包括输入不合适、跨受试者准确率低、参数与时间成本之间不平衡以及缺乏可解释性。最后，我们强调了多方法融合方法的新兴趋势（49.2%的综述论文）并分析了数据及一些示例。我们还对多方法融合的一些挑战提供了见解。我们的综述为未来提高脑电图分类性能的研究奠定了基础。
