深度学习算法在脑电信号解码中的应用

近年来深度学习算法得到飞速发展,在生物医学工程领域的应用也越来越广泛。其中,利用深度学习算法从脑电信号(EEG)中解码生理、心理或病理状态也受到越来越多的关注。综述近年来深度学习算法在EEG解码中的应用,介绍常用算法、典型应用场景、重要进展和现存的问题。首先,论述常用于EEG解码的几类深度学习算法的基本原理,包括卷积神经网络、深度信念网络、自编码器和循环神经网络等。然后,讨论深度学习算法的几个典型EEG解码应用场景,包括脑机接口、情绪与认知识别、疾病辅助诊断。结合应用实例,归纳深度学习算法在EEG解码中的常见问题、解决方案、主要进展和研究趋势。最后,总结深度学习应用于EEG信号解码中仍待解决的一些关键问题,如参数复杂度、训练时间以及泛化能力等。

Deep Learning in EEG Decoding: A Review

In recent years, deep learning algorithms have been developed rapidly, and they are becoming a powerful tool in biomedical engineering. Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological, psychological or pathological states of the brain from EEG. This paper overviews current applications of deep learning algorithms in various EEG decoding tasks, and introduces commonly used algorithms, typical application scenarios, important progresses and existing problems. Firstly, we briefly describe the basic principles of deep learning algorithms used in EEG decoding, including convolutional neural network, deep belief network, auto-encoder and recurrent neural network. Then this paper discusses existing applications of deep learning on EEG, including brain-computer interfaces, cognitive neuroscience and diagnosis of brain disorders. Finally, this paper outlines some key issues that need to be addressed in future applications of deep learning for EEG decoding, such as parameter selection, computational complexity, and the capability of generalization.