Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems

  • Emanuel Morales-Flores
  • Juan Manuel Ramírez-Cortés
  • Pilar Gómez-Gil
  • Vicente Alarcón-Aquino
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 294)


Brain Computer Interfaces (BCI) is the generic denomination of systems aiming to establish communication between a human being and an automated system, based on the electric brain signals detected through a variety of modalities. Among these, electroencephalographic signals (EEG) have received considerable attention due to several factors arising on practical scenarios, such as noninvasiveness, portability, and relative cost, without lost on accuracy and generalization. In this chapter we discuss the characteristics of a typical phenomenon associated to motor imagery and mental tasks experiments, known as event related synchronization and desynchronization (ERD/ERS), as well as its energy distribution in the time-frequency space. The typical behavior of ERD/ERS phenomenon has led proposal of different approaches oriented to the solution of the identification problem. In this work, an architecture based on adaptive neuro-fuzzy inference systems (ANFIS) assembled to a recurrent neural network, applied to the problem of mental tasks temporal classification, is presented. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural network. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network, and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning – extended Kalman filter). A classification rate of 88.12 % in average was obtained through the FCRNN during the generalization stage.


Independent Component Analysis Motor Imagery Recurrent Neural Network Adaptive Neuro Fuzzy Inference System Mental Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emanuel Morales-Flores
    • 1
  • Juan Manuel Ramírez-Cortés
    • 1
  • Pilar Gómez-Gil
    • 2
  • Vicente Alarcón-Aquino
    • 3
  1. 1.Department of ElectronicsNational Institute of Astrophysics, Optics and ElectronicsTonantzintlaMexico
  2. 2.Department of Computer ScienceNational Institute of Astrophysics, Optics and ElectronicsTonantzintlaMexico
  3. 3.Department of ElectronicsUniversity of the AmericasCholulaMexico

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