Dendrite Ellipsoidal Neuron Trained by Stochastic Gradient Descent for Motor Imagery Classification

  • Fernando ArceEmail author
  • Omar Mendoza-Montoya
  • Erik Zamora
  • Javier M. Antelis
  • Humberto Sossa
  • Jessica Cantillo-Negrete
  • Ruben I. Carino-Escobar
  • Luis G. Hernández
  • Luis Eduardo Falcón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)


Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vector machines which are some of the most common classifiers used to detect motor-related information in brain signals. These achieved an accuracy of 72.38% and 65.81%, respectively.


Dendrite Ellipsoidal Neuron Motor Imagery Electroencephalography Multilayer perceptrons Support vector machines Stochastic Gradient Descent k-means++ 



H. Sossa and E. Zamora would like to acknowledge the support provided by CIC-IPN and M. Antelis to Tecnológico de Monterrey, in carrying out this research. This work was economically supported by SIP-IPN (grant numbers 20180180, 20180730, 20190007 and 20190166) and CONACYT grant numbers 65 (Frontiers of Science), 268958 and PN2015-873. F. Arce and O. Mendoza-Montoya acknowledge CONACYT for the scholarship granted towards pursuing their PhD and post-PhD studies, respectively.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fernando Arce
    • 1
    Email author
  • Omar Mendoza-Montoya
    • 2
  • Erik Zamora
    • 1
  • Javier M. Antelis
    • 2
  • Humberto Sossa
    • 1
    • 2
  • Jessica Cantillo-Negrete
    • 3
  • Ruben I. Carino-Escobar
    • 3
  • Luis G. Hernández
    • 2
  • Luis Eduardo Falcón
    • 2
  1. 1.Instituto Politécnico Nacional - CICMexico CityMexico
  2. 2.Tecnológico de Monterrey Campus GuadalajaraZapopanMexico
  3. 3.Division of Medical Engineering ResearchInstituto Nacional de RehabilitaciónMexico CityMexico

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