生物医学工程研究2024，Vol.43Issue(3)：200-206,231,8.DOI:10.19529/j.cnki.1672-6278.2024.03.04
基于稀疏嵌入的多分类脑电信号分类方法研究
Research on multi-classification EEG signal classification method based on sparse embedding
摘要
Abstract
In order to solve the slow transmission rate and low classification accuracy,we proposed a multi-classification method for electroencephalogram(EEG)signals by using one vs rest filter bank common spatial pattern(OVR-FBCSP)and sparse embeddings(SE).For reducing the complexity of multi-task feature extraction and improving the classification efficiency,the OVR-FBCSP was used to extract EEG feature.Then,the corresponding label matrix was embedded in low dimension,the SE model was constructed,and the embedding matrix of the training and test data were calculated respectively.Finally,k-nearest neighbor(kNN)classification was performed for training and test data in the embedding space.The experiment was tested on the BCI Competition IV-2a open data set and compared with other classification methods.Experimental results show that the proposed method has higher classification accuracy and shorter analysis time.关键词
运动想象/稀疏嵌入/一对多共空间模式/k最近邻Key words
Motor imagery/Sparse embedding/One vs rest common spatial pattern/K-nearest neighbor分类
医药卫生引用本文复制引用
郑旭,王延平,高诺..基于稀疏嵌入的多分类脑电信号分类方法研究[J].生物医学工程研究,2024,43(3):200-206,231,8.基金项目
山东省自然科学基金资助项目(ZR2022MF309) （ZR2022MF309）
山东省科技型中小企业创新能力提升工程项目(2022TSGC2554). （2022TSGC2554）
