Neural Networks Preprocessing Based Adaptive Estimation of Evoked Potentials

Author

Zha, Daifeng

Author_Institution

Coll. of Electron. Eng., Jiujiang Univ., Jiujiang

fYear

2008

Firstpage

2206

Lastpage

2208

Abstract

Independent component analysis (ICA) is a new powerful tool for blind source separation. This paper proposes a new algorithm that combines two existent algorithms, the improved infomax algorithm and the fastICA algorithm. Utilizing the initial weights obtained by the improved infomax algorithm, we can not only reduce the length of data which fastICA algorithm needs, but also enhance the convergence stability of fastICA algorithm. The effectiveness of the algorithm is verified by computer simulations.

Keywords

bioelectric potentials; blind source separation; brain; independent component analysis; medical signal processing; neural nets; neurophysiology; blind source separation; evoked potentials; fastICA algorithm; independent component analysis; infomax algorithm; neural networks; Adaptive estimation; Biological neural networks; Blind source separation; Convergence; Educational institutions; Independent component analysis; Neural networks; Power engineering and energy; Signal processing algorithms; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-1747-6

Electronic_ISBN

978-1-4244-1748-3

Type

conf

DOI

10.1109/ICBBE.2008.882

Filename

4535762