论文标题
大脑信号分析基于深度学习方法：非侵入性脑信号研究的最新进展
Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals
论文作者
论文摘要
脑信号构成了数百万脑神经元（神经细胞和脑细胞）处理的信息。可以使用各种非侵入性技术记录和分析这些大脑信号，例如脑电图（EEG），磁脑摄影仪（MEG）以及脑影像技术，例如磁共振成像（MRI），计算机断层扫描（CT）等，这些技术将在本文中进行简短讨论。本文讨论了当前新兴技术，例如用于分析这些大脑信号的不同深度学习（DL）算法的使用以及这些算法如何通过应用信号解码策略来帮助确定人员的神经状况。
Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the Electroencephalograph (EEG), Magneto-encephalograph (MEG) as well as brain-imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL) algorithms for the analysis of these brain signals and how these algorithms will be helpful in determining the neurological status of a person by applying the signal decoding strategy.
