Constrained ICA and its application to removing artifacts in EEG.
- Author:
Ansheng GAO
1
;
Yangyu LUO
;
Ken CHEN
Author Information
1. Departmnent of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, Citina. eqsing@gmail.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artifacts;
Brain;
physiology;
Electroencephalography;
methods;
Humans;
Principal Component Analysis;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2008;25(3):497-501
- CountryChina
- Language:Chinese
-
Abstract:
Independent component analysis (ICA) is a statistic technique which extracts independent components from a set of standard signals. Since Electroencephalogram (EEG) signals are the mixture of several relatively independent sources, ICA has attracted extensive attention in the field of EEG processing. In this paper, a new Constrained ICA (cICA) algorithm is introduced, it would solve the problem of orderless output when FastICA algorithm is used. The experiment results testify that the cICA algorithm can reduce the effect of different individual when the artifacts of EEG are removed manually. The results also show that the cICA algorithm is robust and performs faster convergence.
