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

Fernando Arce, Omar Mendoza-Montoya, Erik Zamora, Javier M. Antelis, Humberto Sossa, Jessica Cantillo-Negrete, Ruben I. Carino-Escobar, Luis G. Hernández, Luis Eduardo Falcón

    Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

    1 Cita (Scopus)

    Resumen

    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 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.

    Idioma originalInglés
    Título de la publicación alojadaPattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings
    EditoresJesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López, Joaquín Salas
    EditorialSpringer Verlag
    Páginas80-88
    Número de páginas9
    ISBN (versión impresa)9783030210762
    DOI
    EstadoPublicada - 1 ene 2019
    Evento11th Mexican Conference on Pattern Recognition, MCPR 2019 - Querétaro, México
    Duración: 26 jun 201929 jun 2019

    Serie de la publicación

    NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volumen11524 LNCS
    ISSN (versión impresa)0302-9743
    ISSN (versión digital)1611-3349

    Conferencia

    Conferencia11th Mexican Conference on Pattern Recognition, MCPR 2019
    País/TerritorioMéxico
    CiudadQuerétaro
    Período26/06/1929/06/19

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