深度学习算法在脑电信号解码中的应用

doi:10.3969/j.issn.0258-8021.2019.04.11

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Abstract 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.

Key words： deep learning neural network EEG decoding brain-computer interface

Received: 09 May 2018

PACS:

R318

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	Articles by authors
	Wei Mengying
	Li Linling
	Huang Gan
	Tang Fei
	Zhang Zhiguo

Cite this article:

Wei Mengying,Li Linling,Huang Gan, et al. Deep Learning in EEG Decoding: A Review[J]. Chinese Journal of Biomedical Engineering, 2019, 38(4): 464-472.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.04.11 OR http://cjbme.csbme.org/EN/Y2019/V38/I4/464

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