基于共空间模式和均匀流形投影的运动想象脑电信号识别方法

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Abstract The recognition and classification of motor imaging EEG signals has always been a hot issue in the field of brain-computer research. In response to this problem, uses a manifold learning method that is different from the traditional linear dimensionality reduction method, combine the CSP and UMAP, make full use of the non-linear features in the EEG signal, perform feature extraction and data dimensionality reduction on the motor imagination EEG signal, and use the KNN classifier to classify, and the classification effect is evaluated; The comparison of the data classification results before and after dimensionality reduction illustrates the advantages and necessity of data dimensionality reduction; The performance of dimensionality reduction results in data visualization is further discussed, and it is found that the visualization effect of feature data after data dimensionality reduction is significantly better than that of data without dimensionality reduction. A new EEG signal recognition method based on CSP and UMAP is proposed, which provides reference value for deep analysis of EEG signals and mining of EEG nonlinear characteristics. The angle of data visualization provides new ideas for the recognition of EEG signals in motor imagination.

Key words： metrology EEG signal motor imagination manifold learning UMAP CSP data dimensionality reduction

Received: 31 May 2021 Published: 20 August 2022

PACS:	TB99
	TB973

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	Articles by authors
	FU Rong-rong
	SUI Jia-xin
	LIU Chong
	ZHANG Yang

Cite this article:

FU Rong-rong,SUI Jia-xin,LIU Chong, et al. Recognition of Motor Imagery Electroencephalogram Signal Based on Common Spatial Pattern and UMAP[J]. Acta Metrologica Sinica, 2022, 43(8): 1103-1108.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/ OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I8/1103

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