Journal of Medical and Biological Engineering

, Volume 37, Issue 1, pp 140–155 | Cite as

EMG Signal Filtering Based on Independent Component Analysis and Empirical Mode Decomposition for Estimation of Motor Activation Patterns

  • Claudio Tapia
  • Omar DaudEmail author
  • Javier Ruiz-del-Solar
Original Article


A method based on independent component analysis (ICA) and empirical mode decomposition (EMD) for processing electromyographic (EMG) signals is proposed. This method is used for determining the motor activation pattern of the lower extremities during walking. The method is evaluated by recording EMG signals from 11 healthy women. The EMG signals used for the analysis are recorded from 16 muscles of both lower limbs during walking. The method consists of preprocessing the EMG signals using principal component analysis, analyzing them using ICA, and finally filtering them using EMD. This approach allows the reconstruction of the original source signals by filtering the components that do not have a muscular origin. These data are then segmented and processed using the Hilbert transform in order to obtain a representation of a gait cycle from all records. The activation patterns obtained with this method are compared to the ones obtained using a conventional method, based on low-pass filtering, and a method based only on EMD filtering. According to the results, the proposed method obtains a better sequence of motor activation during walking. The proposed method is validated by a comparison of the results with kinematic behavior (expressed as the angular movement of the hip, knee, and ankle of each participant) and statistical significance analysis.


Gait Electromyography (EMG) Motor behavior 



The authors would like to thank the personnel and professionals working at the Movement Analysis Laboratory of the Pontificia Universidad Católica de Chile, Santiago, Chile, where the tests and experiments were done.


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Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  • Claudio Tapia
    • 1
    • 2
  • Omar Daud
    • 3
    • 5
    Email author
  • Javier Ruiz-del-Solar
    • 4
  1. 1.Department of Electrical EngineeringUniversidad de ChileSantiagoChile
  2. 2.Facultad de Ciencias de la RehabilitacionUniversidad Andres BelloSantiagoChile
  3. 3.Advanced Mining Technology CenterUniversidad de ChileSantiagoChile
  4. 4.Department of Electrical Engineering and the Advanced Mining Technology CenterUniversidad de ChileSantiagoChile
  5. 5.Center for the Development of Nanoscience and Nanotechnology, CEDENNAUniversidad de Santiago de ChileSantiagoChile

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