[1]吴叶兰,张跃,曹璞刚,等.节律自适应的运动想象脑电空域特征提取方法[J].中国医学物理学杂志,2023,40(10):1270-1277.[doi:DOI:10.3969/j.issn.1005-202X.2023.10.014]
　WU Yelan,ZHANG Yue,CAO Pugang,et al.Rhythm adaption method for extracting spatial features of MI-EEG[J].Chinese Journal of Medical Physics,2023,40(10):1270-1277.[doi:DOI:10.3969/j.issn.1005-202X.2023.10.014]
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节律自适应的运动想象脑电空域特征提取方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第10期
页码:
1270-1277
栏目:
医学信号处理与医学仪器
出版日期:
2023-10-27

文章信息/Info

Title:
Rhythm adaption method for extracting spatial features of MI-EEG
文章编号:
1005-202X（2023）10-1270-08
作者:
吴叶兰; 张跃; 曹璞刚; 廉小亲; 于重重
北京工商大学人工智能学院， 北京 100048
Author(s):
WU Yelan;  ZHANG Yue;  CAO Pugang;  LIAN Xiaoqin;  YU Chongchong
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
关键词:
运动想象脑电信号; 特征提取; 滤波器组共空间模式; 免疫粒子群优化
Keywords:
Keywords: motor imagery electroencephalogram feature extraction filter bank common spatial pattern immune particle swarm optimization
分类号:
1005-202X（2023）10-1270-08
DOI:
DOI:10.3969/j.issn.1005-202X.2023.10.014
文献标志码:
A
摘要:
针对运动想象脑电（MI-EEG）信号个体差异性大，特征质量依赖频带的选择，导致多类MI-EEG信号识别效果差的问题，提出节律自适应的空域特征提取方法。方法：用滤波器组共空间模式（FBCSP）提取多个频带的空域特征，结合免疫粒子群优化算法，对特征提取过程中的频、空参数寻优，实现节律、空域特征提取参数的自适应调整，获取最优节律下的FBCSP空域特征，提升多类MI-EEG信号的识别准确率。结果：本文方法在BCI-IV Dataset 2a、BCI-Ⅲ Dataset 3a数据集上取得85.49%的平均准确率，较原始FBCSP方法提升10.84%。结论：本文方法更好地获取了脑电空域特征，能有效提高分类正确率，为MI-EEG分类提供了新的解决思路。
Abstract:
Abstract: Objective To propose a method for spatial feature extraction based on rhythm adaption for addressing the problem of poor recognition of multi-class motor imagery electroencephalogram (MI-EEG) caused by the individual differences in MI-EEG and the dependence of feature quality on frequency band selection. Methods The spatial features under different frequency bands were extracted with filter bank common spatial pattern (FBCSP). The immune particle swarm optimization algorithm was used to optimize the frequency band and spatial feature extraction parameters in feature extraction for realizing the adaptive adjustment of the rhythm and spatial parameters and obtaining the FBCSP spatial features under the optimal rhythm, thereby improving the recognition accuracy of multi-class MI-EEG. Results The proposed method had an average accuracy of 85.49% on BCI-IV Dataset 2a and BCI-Ⅲ Dataset 3a, which was 10.84% higher than the original FBCSP method. Conclusion The proposed method is advantageous in EEG feature extraction and can effectively improve classification accuracy, providing a new solution for MI-EEG classification.

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备注/Memo

备注/Memo:
【收稿日期】2023-06-05 【基金项目】国家重点研发计划（2018YFC0807903）. 【作者简介】吴叶兰，硕士，副教授，主要研究方向：机器人视觉、高光谱检测，E-mail: wuyel@th.btbu.edu.cn
更新日期/Last Update: 2023-10-27