Recognition of motor imagery EEG patterns based on common feature analysis

被引：6
|
作者：
Huang, Zhenhao [1 ,2 ]
Qiu, Yichun [1 ,3 ]
Sun, Weijun [1 ,4 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China
[2] Guangdong Hong Kong Macao Joint Lab Smart Mfg, Guangzhou, Peoples R China
[3] Minist Educ, Key Lab Intelligent Detect & Internet Things Mfg, Guangzhou, Peoples R China
[4] Guangdong Key Lab IoT Informat Technol, Guangzhou, Peoples R China
来源：
BRAIN-COMPUTER INTERFACES | 2021年 / 8卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Brain-computer interface; motor imagery; common feature analysis; tensor decomposition; BRAIN-COMPUTER INTERFACES; CLASSIFICATION; DISCRIMINATION; COMMUNICATION; SIGNALS; FILTERS;
D O I：
10.1080/2326263X.2020.1783170
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject's mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find reliable and interpretable features associated with specific MI patterns. Evaluation using several open competition datasets justifies that the common features could more accurately identify MI characteristics and hence substantially benefit MI EEG patterns recognition.
引用
收藏
页码：128 / 136
页数：9
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