Shrinkage Estimator based Common Spatial Pattern for Multi-Class Motor Imagery Classification by Hybrid Classifier

被引：0
|
作者：
Sharbaf, Mohammadreza Edalati [1 ]
Fallah, Ali [1 ]
Rashidi, Saeid [2 ]
机构：
[1] Amirkabir Univ Technol, Fac Biomed Engn, Tehran, Iran
[2] Islamic Azad Univ, Sci & Res branch, Fac Biomed Engn, Tehran, Iran
来源：
2017 3RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND IMAGE ANALYSIS (IPRIA) | 2017年
关键词：
BCI; motor imagery; OVO; shrinkage estimator; multi-class; SPECTRAL FILTERS; MOVEMENTS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced One Versus One (OVO) structure to classify EEG-based multi-class motor imagery signals. Also, shrinkage estimator based Common Spatial Pattern (CSP) is used to overcome disadvantages of conventional CSP. Shrinkage estimator is a procedure to estimate covariance matrix that regularizes CSP versus overfitting. The results of four-class classification of BCI competition IV dataset 2a, show that the performance is improved to 0.61 kappa score.
引用
收藏
页码：26 / 31
页数：6
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