Artificial Neural Networks and Common Spatial Patterns for the Recognition of Motor Information from EEG Signals

  • Carlos Daniel Virgilio GonzalezEmail author
  • Juan Humberto Sossa Azuela
  • Javier M. Antelis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)


This paper proposes the use of two models of neural networks (Multi Layer Perceptron and Dendrite Morphological Neural Network) for the recognition of voluntary movements from electroencephalographic (EEG) signals. The proposal consisted of three main stages: organization of EEG signals, feature extraction and execution of classification algorithms. The EEG signals were recorded from eighteen healthy subjects performing self-paced reaching movements. Three classification scenarios were evaluated in each participant: Relax versus Intention, Relax versus Execution and Intention versus Execution. The feature extraction stage was carried out by applying an algorithm known as Common Spatial Pattern, in addition to the statistical methods called Root Mean Square, Variance, Standard Deviation and Mean. The results showed that the models of neural networks provided decoding accuracies above chance level, whereby, it is able to detect a movement prior its execution. On the basis of these results, the neural networks are a powerful promising classification technique that can be used to enhance performance in the recognition of motor tasks for BCI systems based on electroencephalographic signals.


Brain computer interface EEG signals Motor task Common Spatial Pattern 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 and 20180943) and the National Council of Science and Technology of Mexico (CONACyT) (65 Frontiers of Science, numbers 268958 and PN2015-873).


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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
  • Javier M. Antelis
    • 2
  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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