期刊文献+

独立分量分析在生物医学信号处理中的应用 被引量:10

Applications of Independent Component Analysis(ICA)in Biomedical Signal Processing
下载PDF
导出
摘要 独立分量分析是盲信号处理领域的研究热点 ,是盲信号处理的重要组成部分。介绍了独立分量分析 (ICA)的基本模型、数学原理、研究进展 ,以及当前广泛应用的FastICA算法 ,着重论述了ICA在生物医学信号处理中的应用 。 The independent component analysis (ICA) is a hot issue in the field of blind signal processing, and it forms the most part of blind source separation . The basic model, mathematical principles,recent achievements of ICA,and the popular FastICA algorithm are introduced. Several ICA applications on the biomedical signal processing is persented, and the facing problem and the direction of ICA research are discussed.
出处 《生物医学工程研究》 2003年第4期56-60,共5页 Journal Of Biomedical Engineering Research
基金 山东大学跨学科预研基金资助项目
关键词 独立分量分析 生物医学信号 盲信号 ICA FASTICA算法 盲源分离 BSS 脑电信号 Independent component analysis Blind source separation Biomedical signal EEG
  • 相关文献

参考文献13

  • 1[1]Comon P. Independent component analysis, A new concept[J]? Signal Processing,1994,36:287-314.
  • 2[2]Bell A J, Sejnowski T J. An information maximization approach to blind separation and blind deconvolution[J]. Neural Computation,1995,7(6):1129-1159.
  • 3[3]Lee TW. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources[J]. Neural Computation,1999,11(2):409-433.
  • 4[4]Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Trans Neural Networks,1999,10(3):626-634.
  • 5[5]Cardoso JF. High-order contrasts for independent component analysis[J]. Neural Computation,11(1),1999,157-192.
  • 6[6]Nojun Kwak, Chong-Ho Choi. Face recognition using feature extraction based on independent component analysis[J]. IEEE,ICIP 2002,337-340.
  • 7[7]Lieven De Lathauwer, Bart De Moor, Joos Banewale. Fetal ECG extraction by blind source subspace separation[J]. IEEE Transactions on Biomedical Engineering, 2000,47(5):567-572.
  • 8[8]Ricardo Vigario, Jaako Sarela, Beikko Jousmiiki et al. Independent component approach to the analysis of EEG and MEG recordings[J]. IEEE Tansactions on Biomedical Engineering,2000,47(5):589-593.
  • 9周卫东.基于独立分量分析的生理信号盲源分离(英文)[J].山东生物医学工程,2002,21(2):4-6. 被引量:6
  • 10郭双,王汝霖,王怀阳.独立分量分析在脑电信号识别方面的应用[J].山东生物医学工程,2002,21(4):1-4. 被引量:7

二级参考文献38

  • 1杨福生 等.独立分量分析及其在生物医学工程中的应用[D]..99''中国生物医学电子学学术年会论文集[C].,1999..
  • 2[1]Lee T W. Independent component analysis using an extended infomax algorithm for mixed Subgaussian and Supergaussian sources. Neural Computation,1999,11(2):409~433
  • 3[2]Bell A J,Sejnowski T J. An information maximization approach to blind separation and blind deconvolution. Neural Computation,1995,7(6):1129~1159
  • 4[3]Hyvarinen A.Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks,1999,10(3):626~634
  • 5[4]Amari SI,Cichocki A, and Yang H H. A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems MIT press,1996,757~763
  • 6[5]Amari SI. Adaptive blind signal processing-neural network approaches. Proc IEEE, 1998,86(10):2026~2049
  • 7[6]Delfosse N and Loubaton P. Adaptive blind separation of independent sources:a deflation approach. Signal Processing,1995,45(1):59~83
  • 8[7]Cardoso JF. Blind signal separation:statistical principles. Proc IEEE,1998,86(10):2009~2025
  • 9[8]Cichocki A. Robust learning algorithm for blind separation of signals. Electronics Letters,1994,30(17):1386~1387
  • 10[2]Toby Howard. Beyond the big barrier. Personal Computer World,1996.

共引文献33

同被引文献60

引证文献10

二级引证文献33

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部