Electrode Selection Based on k-means for Motor Activity Classification in EEG

  • Rafael Lemuz López Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias de la Computación
  • W. Gómez-López Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias de la Computación
  • I. Ayaquica-Martínez Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias de la Computación
  • C. Guillén-Galván Benemérita Universidad Autónoma de Puebla. Facultad de Ciencias de la Computación

Abstract

 

We present an algorithm for electrodes selection associated with motor imagery activity. The algorithm uses a clustering technique called k-means to form groups of sensors and selects the group corresponding to the highest correlation activity. Then, we evaluate the selected electrodes computing the classification index using the projective decomposition called common spatial patterns and a linear discriminant method in a left hand vs right foot motor imagery classification task. This approach significantly reduces the number of electrodes from 118 to 35 while improving the classification accuracy index.

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Published
2014-05-15
How to Cite
Lemuz López, R., Gómez-López, W., Ayaquica-Martínez, I., & Guillén-Galván, C. (2014). Electrode Selection Based on k-means for Motor Activity Classification in EEG. Mexican Journal of Biomedical Engineering, 35(2), 107-114. Retrieved from https://rmib.mx/index.php/rmib/article/view/197
Section
Research Articles