Apply ICA (independent component analysis) to removing power noise from EEG.
- Author:
Yang GAO
1
;
Baikun WAN
;
Xin ZHU
Author Information
1. Department of Biomedical Engineering, College of Precision Instrument, Tianjin University, Tianjin 300072. gysmile@eyou.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Artifacts;
Electroencephalography;
Humans;
Schizophrenia;
physiopathology;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2003;20(4):713-715
- CountryChina
- Language:Chinese
-
Abstract:
Power noise is constantly found in EEG signals; thus the acquisition and analysis of EEG signals can be strongly influenced. Comparison of the efficiencies of four ICA algorithms (Fastica, Extended Infomax, EGLD, Pearson-ICA) and SVD methods in extracting power noise in the EEG signals showed that ICA algorithms appear insensitive to the noise disturbance, whereas the commonly used SVD method does not. By applying the Extended-Infomax ICA with better convergence in this paper, it was demonstrated that the power noise contained in the 16-channel EEG signals of one Alzheimer-disease patient were removed successfully(the lowest signal-noise-ratio for power noise is 0 dB). ICA has a possible important value and prospect in biomedical signal processing, especially in clinical medical engineering.
