EEG Channel Relevance Analysis Using Maximum Mean Discrepancy on BCI Systems

  • D. F. Luna-Naranjo
  • J. V. Hurtado-Rincon
  • D. Cárdenas-PeñaEmail author
  • V. H. Castro
  • H. F. Torres
  • G. Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Brain-Computer Interfaces bridge the communication between brains and devices. Channel selection as a stage for developing BCI systems allows reducing costs and improve the overall performance. This paper proposes a relevance analysis based on the maximum mean discrepancy as the distance function between a pair of single-channel trials, termed rMMD. The proposed rMMD starts with a trial embedding that highlights temporal dynamics, and ends with a channel ranking according to a designed relevance function. The function relies on the within and between class distances to quantify the discrimination capability of each channel. We evaluate the rMMD on a bi-class motor-imagery (MI) dataset holding 64 channels and more than 40 subjects. In comparison with no channel selection and a heuristic approach, our proposed relevance analysis statistically improves the classification of MI tasks with a reduced set of channels.


Channel selection Time-series relevance analysis Brain computer interface 



This research was supported by the research project 36706 “BrainScore: Sistema compositivo, gráfico y sonoro creado a partir del comportamiento frecuencial de las señales cerebrales”, funded by Universidad de Caldas and Universidad Nacional de Colombia.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • D. F. Luna-Naranjo
    • 1
  • J. V. Hurtado-Rincon
    • 1
  • D. Cárdenas-Peña
    • 1
    Email author
  • V. H. Castro
    • 2
  • H. F. Torres
    • 2
  • G. Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Universidad de CaldasManizalesColombia

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