Signal processing algorithms for motor imagery brain-computer interface:State of the art

被引：3
|
作者：
Hong, Jie [1 ]
Qin, Xiansheng [1 ]
Li, Jing [1 ]
Niu, Junlong [1 ]
Wang, Wenjie [1 ]
机构：
[1] Northwestern Polytech Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2018年 / 35卷 / 06期
关键词：
Motor imagery brain-computer interface (MI-BCI); signal processing algorithms; pre-processing; feature extraction; classification; EEG SIGNALS; CLASSIFICATION; FEEDBACK; PERFORMANCE; MOVEMENTS; CALIBRATION; PATTERNS; FEATURES; TASKS; BCIS;
D O I：
10.3233/JIFS-181309
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Over the past two decades, motor imagery brain-computer interface (MI-BCI) system has been extensively developed. In this system signal processing algorithms are critical to robust operation. In BCI community, however, there is no comprehensive review of the recent development of signal processing algorithms. Through analyzing the latest papers, signal processing algorithms of pre-processing, feature extraction, feature selection, and classification components are discussed in detail. Besides, post-processing and other existing problems are mentioned. The following key issues are addressed: (1) which components are the key of signal processing; (2) which signal processing algorithms are frequently used in each component; (3) which signal processing algorithms attract more attention. This information can be used as reference and guidance for further research.
引用
收藏
页码：6405 / 6419
页数：15
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