基于改进Transformer模型的运动想象脑电分类方法研究

doi:10.3969/j.issn.1000-1158.2023.07.22

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Abstract The motor imagery (MI) EEG signal itself is a signal sequence consisting of a set of long and continuous feature values. Traditional Transformers cannot capture dependencies between longer sequences, and setting fixed length sequences can lead to fragmentation issues, so further adjustments and optimization are needed. To address the above issues, a fragment reuse loop mechanism and a relative position encoding mechanism for fragment information have been added to traditional Transformers. This can enable the Transformer to learn feature information from longer feature sequences. At the same time, it can also solve problems such as confusion and reuse of positional encoding information between reused fragments. Subsequently, parallel multi branch CNN was used to further capture local EEG features. Finally, the performance of the improved Transformer model was evaluated using the competition dataset 2008 BCI-Competition 2A. The results showed that, without any manual feature extraction, the average accuracy and kappa value of the improved Transformer model for the four classification dataset were 94.27% and 87.34%, respectively.

Key words： metrology EEG signal motor lmagery BCI convolutional neural networks transformer model loop mechanism for segment relative position encoding

Received: 28 December 2022 Published: 17 July 2023

PACS:	TB99
	TB973

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	Articles by authors
	LIU Yue-feng
	LIU Hao-feng
	WANG Yue
	LIU Bo
	BAO Xiang

Cite this article:

LIU Yue-feng,LIU Hao-feng,WANG Yue, et al. Research on Motor Imagery EEG Classification Method based on Improved Transformer[J]. Acta Metrologica Sinica, 2023, 44(7): 1147-1153.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.07.22 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I7/1147

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