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Complex Empirical Mode Decomposition for Multichannel Information Fusion

  • Danilo Mandic
  • George Souretis
  • Wai Yie Leong
  • David Looney
  • Marc M. Van Hulle
  • Toshihisa Tanaka

Information “fusion” via signal “fission” is addressed in the framework of empirical mode decomposition (EMD). In this way, a general nonlinear and non-stationary signal is first decomposed into its oscillatory components (fission); the components of interest are then combined in an ad hoc or automated fashion to provide greater knowledge about a process in hand (fusion). The extension to the field of complex numbers C is particularly important for the analysis of phase-dependent processes, such as those coming from sensor arrays. This allows us to combine the data driven nature of EMD with the power of complex algebra to model amplitude-phase relationships within multichannel data. The analysis shows that the extensions of EMD to C are not straightforward and that they critically depend on the criterion for finding local extrema within a complex signal. For rigour, convergence of EMD is addressed within the framework of fixed point theory. Simulation examples on information fusion for brain computer interface (BCI) support the analysis.

Keywords

Empirical Mode Decomposition Instantaneous Frequency Information Fusion Intrinsic Mode Function Brain Computer Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Danilo Mandic
    • 1
  • George Souretis
    • 1
  • Wai Yie Leong
    • 2
  • David Looney
    • 1
  • Marc M. Van Hulle
    • 3
  • Toshihisa Tanaka
    • 4
  1. 1.Imperial College LondonLondonUK
  2. 2.Agency for Science, Technology and ResearchSingapore
  3. 3.Katholieke Universiteit LeuvenBelgium
  4. 4.Tokyo University of Agriculture and TechnologyJapan

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