Review on identity feature extraction methods based on electroencephalogram signals.
10.7507/1001-5515.202102057
- Author:
Wenxiao ZHONG
1
;
Xingwei AN
1
;
Yang DI
1
;
Lixin ZHANG
1
;
Dong MING
1
Author Information
1. Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China.
- Publication Type:Review
- Keywords:
electroencephalogram;
feature extraction;
identity identification;
inter-channel features;
single-channel features
- MeSH:
Electroencephalography
- From:
Journal of Biomedical Engineering
2021;38(6):1203-1210
- CountryChina
- Language:Chinese
-
Abstract:
Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
