Wavelet Lifting over Information-Based EEG Graphs for Motor Imagery Data Classification

  • Javier Asensio-CuberoEmail author
  • John Q. Gan
  • Ramaswamy Palaniappan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8908)


The imagination of limb movements offers an intuitive paradigm for the control of electronic devices via brain computer interfacing (BCI). The analysis of electroencephalographic (EEG) data related to motor imagery potentials has proved to be a difficult task. EEG readings are noisy, and the elicited patterns occur in different parts of the scalp, at different instants and at different frequencies. Wavelet transform has been widely used in the BCI field as it offers temporal and spectral capabilities, although it lacks spatial information. In this study we propose a tailored second generation wavelet to extract features from these three domains. This transform is applied over a graph representation of motor imaginary trials, which encodes temporal and spatial information. This graph is enhanced using per-subject knowledge in order to optimise the spatial relationships among the electrodes, and to improve the filter design. This method improves the performance of classifying different imaginary limb movements maintaining the low computational resources required by the lifting transform over graphs. By using an online dataset we were able to positively assess the feasibility of using the novel method in an online BCI context.


Multiresolution analysis EEG data graph representation Motor imagery Brain computer interfacing Wavelet lifting Mutual information 



The first author would like to thank the EPSRC for funding his Ph.D. study via an EPSRC DTA award.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Javier Asensio-Cubero
    • 1
    Email author
  • John Q. Gan
    • 1
  • Ramaswamy Palaniappan
    • 2
  1. 1.University of EssexColchester, EssexUK
  2. 2.University of WolverhamptonTelfordUK

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