Quasi-Newton iteration algorithm for ICA and its application in VEP feature extraction.
- Author:
Xiao'ou LI
1
;
Zhaohui JIANG
;
Xiaowei ZHANG
;
Huanqing FENG
Author Information
1. Department of Electronic Science & Technology, USTC, Hefei 230026, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Event-Related Potentials, P300;
physiology;
Evoked Potentials, Visual;
physiology;
Humans;
Pattern Recognition, Automated;
methods;
Principal Component Analysis;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2006;23(1):45-48
- CountryChina
- Language:Chinese
-
Abstract:
Some noises still exist in the single-trial averaged visual evoked potentials (VEP), so further extraction of the above results is of significance. Independent component analysis (ICA)can separate the sources from their mixtures and make the output statistically as independent as possible; it can remove noises effectively. In this paper, the principle, experiment analyses and results of ICA based on quasi-Newton iteration rule for VEP feature extraction are introduced, It is compared with the fixed-point FastICA algorithm. The experiment results show that the provided algorithm may reinforce signals effectively and extract distinct P300 from the single-trial averaged VEP. It is of good applicability.
