论文标题
脑电图的深度学习：最后十年关键时期
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
论文作者
论文摘要
深度学习在广泛的领域中取得了出色的表现，尤其是在语音识别和计算机视觉上。脑电图的工作相对较少，但是在过去十年中，取得了重大进展。由于缺乏对脑电图中深度学习的全面和主题的全面和主题，我们试图总结最近的进展，以提供概述，以及未来发展的观点。首先，我们简要提及脑电信号的删除工件，然后引入已在脑电图处理和分类中使用的深度学习模型。随后，通过将它们分类为诸如脑部计算机界面，疾病检测和情绪识别之类的群体来审查脑电图中深度学习的应用。紧随其后的是讨论，其中提出了深度学习的利弊，并提出了脑电图中深度学习的未来方向和挑战。我们希望本文可以作为脑电图中深度学习的过去工作的摘要，以及基于深度学习的脑电图研究的进一步发展和成就的开始。
Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.
