Dynamic pruning group equivariant network for motor imagery EEG recognition

Frontiers in Bioengineering and Biotechnology

Bioengineering and Biotechnology

Xianlun Tang¹  Cong Tan¹  Mi Zou¹  Huiming Wang¹  Wei Zhang¹  Tianzhu Wang¹  Zihui Xu² 

[1] Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;Xinqiao Hospital, Army Medical University, Chongqing, China;

关键词: motor imagery;    group convolution network;    prune;    short-time Fourier transform;    deep learning;    BCI;   

DOI  :  10.3389/fbioe.2023.917328

 received in 2022-04-11, accepted in 2023-04-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them.Methods: In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed. Group convolutional networks can learn powerful representations based on symmetric patterns, but they lack clear methods to learn meaningful relationships between them. The dynamic pruning equivariant group convolution proposed in this paper is used to enhance meaningful symmetric combinations and suppress unreasonable and misleading symmetric combinations. At the same time, a new dynamic pruning method is proposed to dynamically evaluate the importance of parameters, which can restore the pruned connections.Results and Discussion: The experimental results show that the pruning group equivariant convolution network is superior to the traditional benchmark method in the benchmark motor imagery EEG data set. This research can also be transferred to other research areas.

【 授权许可】

Unknown   
Copyright © 2023 Tang, Zhang, Wang, Wang, Tan, Zou and Xu.