A Novel Feature Selection Method For Motor Imagery-Based Brain-Computer Interfaces

被引：0

作者：

Momeny, Saeed ^{[1
]}

Faradji, Farhad ^{[1
]}

机构：

[1] KN Toosi Univ Technol, Elect Engn Dept, Tehran, Iran

来源：

26TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE 2018) | 2018年

关键词：

Brain-Computer Interface (BCI); Motor Imagery (MI); Common Spatial Pattern (CSP; Principle Component Analysis (PCA); minimal Redundancy Maximal Relevance (mRAIR);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Feature selection in brain -computer interface (BC!) systems is an important stage that can improve the system performance especially in the presence of a big number of features extracted. In this paper, a new feature selection method is proposed which is a combination of PCA and mRMR. CSP and SVM are used for feature extraction and classification, respectively. The results show that our proposed method for feature selection has a better performance than PCA and mRMR methods.

引用

页码：1421 / 1424

页数：4

共 14 条

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