Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition.
10.7507/1001-5515.202012017
- Author:
Rui ZHANG
1
;
Jiajun LIU
1
;
Mingming CHEN
1
;
Lipeng ZHANG
1
;
Yuxia HU
1
Author Information
1. Henan Key Laboratory of Brain Science & Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China.
- Publication Type:Journal Article
- Keywords:
autocorrelation coefficient threshold;
electrooculogram artifacts removal;
ensemble empirical mode decomposition;
single channel electroencephalogram;
wavelet transform
- MeSH:
Algorithms;
Artifacts;
Computer Simulation;
Electroencephalography;
Signal Processing, Computer-Assisted;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2021;38(3):473-482
- CountryChina
- Language:Chinese
-
Abstract:
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
