论文信息 - A Method for Fast Estimation of Evoked Potentials Based on Independent Component Analysis

A Method for Fast Estimation of Evoked Potentials Based on Independent Component Analysis

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.

Wenhong Liu | Ting Li | Tianshuang Qiu | Anqing Zhang | Xuxiu Zhang

[1] Aapo Hyvärinen,et al. A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[2] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[3] Aapo Hyvärinen,et al. Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[4] James V. Stone. Independent Component Analysis: A Tutorial Introduction , 2007 .

[5] E. Oja,et al. Independent Component Analysis , 2013 .

[6] Allan Kardec Barros,et al. independent , 2006, Gumbo Ya Ya.

[7] Andrzej Cichocki,et al. Noise reduction in brain evoked potentials based on third-order correlations , 2001, IEEE Transactions on Biomedical Engineering.