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A reference-based blind source extraction algorithm

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Abstract

By utilizing a priori information available as reference, constrained independent component analysis (cICA) or independent component analysis with reference (ICA-R) achieves some advantages over other methods. However, ICA-R is very time-consuming; moreover, it is very difficult to determine its threshold parameter, once the value is improperly chosen the algorithm will fail to converge. In order to overcome these drawbacks, a very simple blind source extraction method, whose optimization function is simply the closeness measure between the desired output and its corresponding reference in ICA-R, is proposed in this paper. Experiments with synthesized data and real-world electrocardiograph data confirm its validity and superiority.

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Notes

  1. P = (p1, p2, …,p m ) is called a canonical basis vector, if p i  = 1 and p l  = 0,∀l ≠ i [1].

  2. Since s i ’ are mutually independent and we assume E{s 2 i } = 1, we have E{ss T} = I. \(\tilde{\bf{x}}\) is whitened, so we have \( E\{{\tilde{\bf{x}}\tilde{\bf{x}}}^{T} \} = \left({{\bf{VA}}} \right)E\{{\bf{ss}}^{T} \} \left({{\bf{VA}}} \right)^{T} = \left({{\bf{VA}}} \right)\left({{\bf{VA}}} \right)^{T} = {\bf{I}}. \) Hence, VA is orthogonal. Due to ||w|| = 1 and VA being a unitary matrix, we can get ||p|| = ||w T VA|| = ||w T|| = 1. This is why any p in Table 1 is near to a canonical basis vector.

References

  1. Barros AK, Cichochi A (2001) Extraction of specific signals with temporal structure. Neural Comput 13:1995–2003

    Article  MATH  Google Scholar 

  2. Cichochi A, Amari S (2004) Adaptive blind signal and image processing: learning algorithms and applications. Wiley, New York

    Google Scholar 

  3. De Moor D (1997) Daisy: database for the identification of systems. Available http://www.esat.kuleuven.ac.be/sista/daisy

  4. Huang DSh, Mi JX (2007) A new constrained independent component analysis method. IEEE Trans Neural Netw 18:1532–1535

    Article  Google Scholar 

  5. Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Transn Neural Netw 10:626–634

    Article  Google Scholar 

  6. Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13:411–430

    Article  Google Scholar 

  7. Ille N, Berg R, Scherg M (2001) Spatially constrained independent analysis for artifact correction in EEG and MEG. Neuroimage 13:S159

    Article  Google Scholar 

  8. Jafari MG, Chambers JA (2005) Fetal electrocardiogram extraction by sequential source separation in the wavelet domain. IEEE Trans Biomed Eng 52:390–400

    Article  Google Scholar 

  9. James CJ, Gibson OJ (2003) Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng 50:1108–1116

    Article  Google Scholar 

  10. Lin Q-H, Zheng Y-R, Yin F-L, Liang H, Calhoun VD (2007) A fast algorithm for one-unit ICA-R. Inform Sci 177:1265–1275

    Article  MathSciNet  Google Scholar 

  11. Lu W, Rajapakse JC (2005) Approach and applications of constrained ICA. IEEE Trans Neural Netw 16:203–212

    Article  Google Scholar 

  12. Lu W, Rajapakse JC (2006) ICA with reference. Neurocomputing 69:2244–2257

    Article  Google Scholar 

  13. Zhang ZL (2008) Morphologically constrained ICA for extracting weak temporally correlated signals. Neurocomputing 71:1669–1679

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable and constructive comments, which greatly improved the manuscript’s readability. This work was supported by NSFC under Grant No. 60736009.

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Authors and Affiliations

  1. Key Laboratory for Radar Signal Processing, Xidian University, 710071, Xi’an, China

    Changli Li & Guisheng Liao

  2. School of Information Engineering, Guangdong Ocean University, 524088, Zhanjiang, China

    Changli Li

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  1. Changli Li
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  2. Guisheng Liao
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Correspondence to Changli Li.

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Li, C., Liao, G. A reference-based blind source extraction algorithm. Neural Comput & Applic 19, 299–303 (2010). https://doi.org/10.1007/s00521-009-0303-x

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  • DOI: https://doi.org/10.1007/s00521-009-0303-x

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