J Neural Eng. 2019 Jun;16(3):031001. doi: 10.1088/1741-2552/ab0ab5. Epub 2019 Feb 26.
Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks?
A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture.
For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review.
This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.
脑电图（EEG）分析一直是神经科学中的重要工具，应用于神经科学、神经工程（例如脑机接口，BCI），甚至商业应用。许多脑电图研究中使用的分析工具都使用机器学习来揭示神经分类和神经影像学的相关信息。最近，大型脑电图数据集的可用性和机器学习的进步都导致了深度学习架构的部署，特别是在脑电图信号的分析和理解它可能包含的大脑功能信息方面。这些信号的稳健自动分类是使脑电图在许多应用中更实用且减少对训练有素的专业人员的依赖的重要步骤。为此，我们对深度学习在脑电图分类中的应用进行了系统的文献综述，以解决以下关键问题：（1）哪些脑电图分类任务已经使用深度学习进行了探索？（2）为了训练深度网络，使用了哪些输入公式？（3）是否有特定的深度学习网络结构适合特定类型的任务？
我们在 Web of Science 和 PubMed 数据库上对使用深度学习的脑电图分类进行了系统的文献综述，共确定了 90 项研究。这些研究根据任务类型、脑电图预处理方法、输入类型和深度学习架构进行了分析。
对于脑电图分类任务，卷积神经网络、循环神经网络、深度置信网络在分类准确性方面优于堆叠自动编码器和多层感知机神经网络。使用深度学习的任务分为五类：情感识别、运动想象、心理工作量、癫痫发作检测、事件相关电位检测和睡眠评分。对于每种类型的任务，我们描述了通过本综述发现的特定输入公式、主要特征和最终分类器推荐。
本综述总结了深度学习在脑电图分类中的当前实践和性能结果。提供了许多超参数选择的实用建议，希望能促进或指导深度学习在未来研究中应用于脑电图数据集。
