EEG PATTERN RECOGNITION: Application to a Real Time Control System for Android-Based Mobile Devices

  • Liliana Gutiérrez-Flores
  • Carlos Avilés-Cruz
  • Juan Villegas-Cortez
  • Andrés Ferreyra-Ramírez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


This paper describes a new EEG pattern recognition methodology in Brain Computer Interface (BCI) field. The EEG signal is analyzed in real time looking for detection of “intents of movement”. The signal is processed at specific segments in order to classify mental tasks then a message is formulated and sent to a mobile device to execute a command. The signal analysis is carried out through eight frequency bands within the range of 0 to 32 Hz. A feature vector is conformed using histograms of gradients according to 4 orientations, subsequently the features feed a Gaussian classifier. Our methodology was tested using BCI Competition IV data sets I. For “intents of movements” we detect up to 95% with 0.2 associated noise, with mental task differentiation around 99%. This methodology has been tested building a prototype using an Android based mobile telephone and data gathered with an EPOC Emotive headset, showing very promising results.


EEG Pattern Recognition Self-Paced Control BCI application Mental Tasks Differentiation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liliana Gutiérrez-Flores
    • 1
  • Carlos Avilés-Cruz
    • 1
  • Juan Villegas-Cortez
    • 1
  • Andrés Ferreyra-Ramírez
    • 1
  1. 1.Departamento de ElectrónicaUniversidad Autónoma Metropolitana, AzcapotzalcoMéxico, D.F.Mexico

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