A robust approach to independent component analysis and its application in the analysis of magnetoencephalographic data / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 648-652, 2006.
Article
in Chinese
| WPRIM
| ID: wpr-249537
ABSTRACT
Independent component analysis (ICA) is a new method of signal statistical processing and widely used in many fields. We face several problems such as the different nature of source signals (e.g. both super-Gaussian and sub-Gaussian sources exist), unknown number of sources and contamination of the sensor signals with a high level of additive noise in the analysis of signal. A robust approach was proposed to solve these problems in this paper. Firstly, observations (noisy data) possessing high dimensionality were preprocessed and decomposed into a source signal subspace and a noise subspace. Then the number of sources was got through the cross-validation method, and this solved the problem that ICA could not confirm the number of sources. At last the transformed low-dimensional source signals were further separated with the fast and stable ICA algorithm. Through the analysis of artificially synthesized data and the real-world Magnetoencephalographic data, the efficacy of this robust approach was illustrated.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Signal Processing, Computer-Assisted
/
Magnetoencephalography
/
Principal Component Analysis
/
Methods
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2006
Type:
Article
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