[1]朱湛,王娆芬,吴健珍.基于OEDCNet的运动想象脑电信号分类算法[J].中国医学物理学杂志,2026,43(5):652-659.[doi:DOI:10.3969/j.issn.1005-202X.2026.05.013]
　ZHU Zhan,WANG Raofen,WU Jianzhen.OEDCNet-based motor imagery electroencephalogram signal classification algorithm[J].Chinese Journal of Medical Physics,2026,43(5):652-659.[doi:DOI:10.3969/j.issn.1005-202X.2026.05.013]
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基于OEDCNet的运动想象脑电信号分类算法()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第5期
页码:
652-659
栏目:
医学信号处理与医学仪器
出版日期:
2026-05-28

文章信息/Info

Title:
OEDCNet-based motor imagery electroencephalogram signal classification algorithm
文章编号:
1005-202X（2026）05-0652-08
作者:
朱湛; 王娆芬; 吴健珍
上海工程技术大学电子电气工程学院， 上海 201620
Author(s):
ZHU Zhan;  WANG Raofen;  WU Jianzhen
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
关键词:
脑电信号; 运动想象; 重叠分段重组; 高效通道注意力; 因果膨胀卷积神经网络
Keywords:
Keywords: electroencephalography signal motor imagery overlapping segmentation-recombination efficient channel attention dilated causal convolutional neural network
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2026.05.013
文献标志码:
A
摘要:
针对运动想象脑电样本量不足、跨通道冗余，导致传统模型特征提取不充分的问题，提出一种用于运动想象脑电信号分类的OEDCNet算法。首先提出重叠分段重组的方式优化数据增强策略，通过对原始脑电信号进行重叠分割与重组处理，在扩充样本量的同时缓解过拟合风险，得到增强后的样本数据。然后利用高效通道注意力模块为不同的通道分配最优权重，并抑制冗余通道的干扰信息。通过对EEGNet方法进行改进，提出因果膨胀卷积神经网络模型，实现多尺度特征提取与长时序依赖感知。最终构建的OEDCNet模型在BCI Competition IV-2a数据集上的平均准确率为77.08%，Kappa值为0.689，优于其他模型。结果表明所提的OEDCNet方法具备更优的分类效果，可为运动想象脑电信号的研究提供新的技术路径。
Abstract:
Abstract: To address the problems of insufficient sample size and cross-channel redundancy in motor imagery electroencephalogram (EEG), which leads to inadequate feature extraction in traditional models, a novel OEDCNet algorithm for motor imagery EEG signal classification is proposed. An overlapping segmentation-recombination approach is first introduced to optimize the data augmentation strategy. By subjecting the original EEG signals to overlapping segmentation and subsequent recombination, this approach expands the sample size while mitigating overfitting risks, thereby generating augmented sample data. Subsequently, an efficient channel attention mechanism is utilized to assign optimal weights to different channels and suppress interference from redundant channels. Finally, by improving the EEGNet approach, a dilated causal convolutional neural network is established to enable multi-scale feature extraction and long-range temporal dependency perception. The constructed OEDCNet model achieves an average accuracy of 77.08% and a Kappa value of 0.689 on the BCI Competition IV-2a dataset, outperforming other models. Experimental results demonstrate that the proposed OEDCNet method exhibits superior classification performance, thus providing a new technical pathway for the research on motor imagery EEG signal.

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备注/Memo

备注/Memo:
【收稿日期】2025-12-24 【基金项目】江西省卫健委重点科技项目（2023ZD008）；上海市科学技术委员会地方院校能力建设项目（23010501700） 【作者简介】朱湛，硕士研究生，研究方向：运动想象脑电信号，E-mail:1456297195@qq.com 【通信作者】吴健珍，博士，讲师，研究方向：脑机接口、数字图像处理，E-mail: wjz0796@163.com
更新日期/Last Update: 2026-05-29