Short Time EEG Connectivity Features to Support Interpretability of MI Discrimination

  • V. GómezEmail author
  • A. Álvarez
  • P. Herrera
  • G. Castellanos
  • A. Orozco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Brain connectivity analysis during motor imagery (MI) tasks has evolved as an essential and promising tool for its use in brain-computer interfaces (BCI). Many approaches devoted to BCI systems focus on the distinction between different MI tasks from electroencephalogram (EEG) signals. However, given the non-stationarity of the brain activity, the MI discrimination yields to different classification performances between subjects. Here, we introduced an MI discrimination system from EEG signals to reveal relevant brain connectivity patterns associated with a specific MI protocol. Indeed, we employ a windowed-based feature representation using the well-known Common Spatial Pattern (CSP) technique. Then, the classification performance along temporal windows is related to a Phase Locking Value (PLV)-based connectivity measure. Obtained results show a remarkable relationship between high classification performances and the subject coupling with the acquisition protocol concerning the windows that present the MI stimulus.


Electroencephalogram Motor Imagery Brain connectivity 



This study was supported by the projects 1110-744-55778 and 6-18-1 funded by Colciencias and Universidad Tecnológica de Pereira, respectively. V. Gómez-Orozco was supported by the program “Doctorado Nacional en Empresa - Convoctoria 758 de 2016”, funded by Colciencias. A. Orozco was supported by the Master in Electrical Engineering from Universidad Tecnológica de Pereira.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • V. Gómez
    • 1
    Email author
  • A. Álvarez
    • 1
  • P. Herrera
    • 2
  • G. Castellanos
    • 3
  • A. Orozco
    • 1
  1. 1.Automatic Research Group, Faculty of EngineeringsUniversidad Tecnológica de PereiraPereiraColombia
  2. 2.Psychiatry, Neuroscience, and Community Group, School of MedicineUniversidad Tecnológica de PereiraPereiraColombia
  3. 3.Signal Proccesing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

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