A review of researches on electroencephalogram decoding algorithms in brain-computer interface.
10.7507/1001-5515.201812049
- Author:
Xiaoyu ZHOU
1
;
Minpeng XU
2
,
3
;
Xiaolin XIAO
1
;
Long CHEN
4
;
Xiaosong GU
2
,
4
;
Dong MING
2
,
4
Author Information
1. School of Precision Instrument and Opto-electronics Engineering, TianJin University, TianJin 300072, P.R.China.
2. School of Precision Instrument and Opto-electronics Engineering, TianJin University, TianJin 300072, P.R.China
3. Academy of Medical Engineering and Translational Medicine. TianJin University, TianJin 300072, P.R.China.minpeng.xu@tju.edu.cn.
4. Academy of Medical Engineering and Translational Medicine. TianJin University, TianJin 300072, P.R.China.
- Publication Type:Journal Article
- Keywords:
brain-computer interface;
electroencephalogram;
feature extraction;
pattern recognition
- MeSH:
Algorithms;
Brain;
physiology;
Brain-Computer Interfaces;
Electroencephalography;
Humans;
Pattern Recognition, Automated
- From:
Journal of Biomedical Engineering
2019;36(5):856-861
- CountryChina
- Language:Chinese
-
Abstract:
Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
