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Motor Intention Recognition in EEG: In Pursuit of a Relevant Feature Set

  • Pablo A. Iturralde
  • Martín Patrone
  • Federico Lecumberry
  • Alicia Fernández
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

Brain-computer interfaces (BCIs) based on electroencephalograms (EEG) are a noninvasive and cheap alternative to get a communication channel between brain and computers. Some of the main issues with EEG signals are its high dimensionality, high inter-user variance, and non-stationarity. In this work we present different approaches to deal with the high dimensionality of the data, finding relevant descriptors in EEG signals for motor intention recognition: first, a classical dimensionality reduction method using Diffusion Distance, second a technique based on spectral analysis of EEG channels associated with the frontal and prefrontal cortex, and third a projection over average signals. Performance analysis for different sets of features is done, showing that some of them are more robust to user variability.

Keywords

Average Signal High Dimensionality Rapid Serial Visual Presentation Target Class Relevant Descriptor 
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 2012

Authors and Affiliations

  • Pablo A. Iturralde
    • 1
  • Martín Patrone
    • 1
  • Federico Lecumberry
    • 2
  • Alicia Fernández
    • 2
  1. 1.Department of Physics, School of EngineeringUdelaRMontevideoUruguay
  2. 2.Department of Electrical Engineering, School of EngineeringUdelaRMontevideoUruguay

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