Classification of Motor Imagery EEG Signals with CSP Filtering Through Neural Networks Models

  • Carlos Daniel Virgilio GonzalezEmail author
  • Juan Humberto Sossa Azuela
  • Elsa Rubio Espino
  • Victor H. Ponce Ponce
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)


The paper reports the development and evaluation of brain signals classifiers. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals used, represent four motor actions: Left Hand, Right Hand, Tongue and Foot movements; in the frame of the Motor Imagery Paradigm. These EEG signals were obtained from a database provided by the Technological University of Graz. From this dataset, only the EEG signals of two healthy subjects were used to carry out the proposed work. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical method called Root Mean Square. The classification algorithms used were: K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron and Dendrite Morphological Neural Networks. This algorithms was evaluated with two studies. The first one aimed to evaluate the performance in the recognition between two classes of Motor Imagery tasks; Left Hand vs. Right Hand, Left Hand vs. Tongue, Left Hand vs. Foot, Right Hand vs. Tongue, Right Hand vs. Foot and Tongue vs. Foot. The second study aimed to employ the same algorithms in the recognition between four classes of Motor Imagery tasks; Subject 1 - \(93.9\% \pm 3.9\%\) and Subject 2 - \(68.7\% \pm 7\%\).


EEG signals Motor Imagery Common Spatial Pattern RMS One vs Rest Pair-Wise Dendrite Morphological Neural Network Multilayer Perceptron 



We would like to express our sincere appreciation to the Instituto Politécnico Nacional and the Secretaria de Investigación y Posgrado for the economic support provided to carry out this research. This project was supported economically by SIP-IPN (numbers 20180730, 20180943 and 20180846) and CONACYT (65 Frontiers of Science).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Carlos Daniel Virgilio Gonzalez
    • 1
    Email author
  • Juan Humberto Sossa Azuela
    • 1
    • 2
  • Elsa Rubio Espino
    • 1
  • Victor H. Ponce Ponce
    • 1
  1. 1.Centro de Investigación en Computación – Instituto Politécnico NacionalMexico CityMexico
  2. 2.Tecnológico de Monterrey, Escuela de Ingeniería y CienciasZapopanMexico

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