Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis.
10.7507/1001-5515.202111060
- Author:
Hao SONG
1
;
Song XU
2
;
Guoming LIU
2
;
Jing LIU
3
;
Peng XIONG
1
Author Information
1. Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P. R. China.
2. Beijing Aerospace Automatic Control Institute, Beijing 100854, P. R. China.
3. College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, P. R. China.
- Publication Type:Journal Article
- Keywords:
Electroencephalography;
Filter;
Independent component analysis;
Non-invasive brain computer interface;
Ocular artifact removal
- MeSH:
Humans;
Electrooculography/methods*;
Artifacts;
Brain-Computer Interfaces;
Algorithms;
Electroencephalography/methods*;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2022;39(6):1074-1081
- CountryChina
- Language:Chinese
-
Abstract:
The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.
