Using Morphological-Linear Neural Network for Upper Limb Movement Intention Recognition from EEG Signals

Gerardo Hernández, Luis G. Hernández, Erik Zamora, Humberto Sossa, Javier M. Antelis, Omar Mendoza-Montoya, Luis E. Falcón

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

    1 Cita (Scopus)

    Resumen

    This study aims to compare classical and Morphological-Linear Neural Network (MLNN) algorithms for the intention recognition to perform different movements from electroencephalographic (EEG) signals. Three classification models were implemented and assessed to decode EEG motor imagery signals: (i) Morphological-Linear Neural Network (MLNN) (ii) Support Vector Machine (SVM) and (iii) Multilayer perceptron (MLP). Real EEG signals recorded during robot-assisted rehabilitation therapy were used to evaluate the performance of the proposed algorithms in the classification of three classes (relax, movement intention A Int A and movement intention B Int B) using multi-CSP based features extracted from EEG signals. The results of a ten-fold cross validation show similar results in terms of classification accuracy for the SVM and MLNN models. However, the number of parameters used in each model varies considerably (the MLNN model use less parameters than the SVM). This study indicates potential application of MLNNs for decoding movement intentions and its use to develop more natural and intuitive robot assisted neurorehabilitation therapies.

    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
    Páginas389-397
    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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