A First Attempt at Constructing Genetic Programming Expressions for EEG Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "C{\'e}sar Est{\'e}banez and 
                 Jos{\'e} Mar\'{\i}a Valls and Ricardo Aler and In{\'e}s Mar\'{\i}a Galv{\'a}n",
  title =        "A First Attempt at Constructing Genetic Programming
                 Expressions for EEG Classification",
  year =         "2005",
  pages =        "665--670",
  keywords =     "genetic algorithms, genetic programming, EEG, BCI,
                 brain computer interface, projection",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  editor =       "Wlodzislaw Duch and Janusz Kacprzyk and Erkki Oja and 
                 Slawomir Zadrozny",
  booktitle =    "Artificial Neural Networks: Biological Inspirations -
                 ICANN 2005, 15th International Conference, 2005,
                 Proceedings, Part I",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3696",
  ISBN =         "3-540-28752-3",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3696&spage=665",
  DOI =          "doi:10.1007/11550822_103",
  address =      "Warsaw, Poland",
  month =        "11-15 " # sep,
  abstract =     "In BCI (Brain Computer Interface) research, the
                 classification of EEG signals is a domain where raw
                 data has to undergo some preprocessing, so that the
                 right attributes for classification are obtained.
                 Several transformational techniques have been used for
                 this purpose: Principal Component Analysis, the
                 Adaptive Autoregressive Model, FFT or Wavelet
                 Transforms, etc. However, it would be useful to
                 automatically build significant attributes appropriate
                 for each particular problem. we use Genetic Programming
                 to evolve projections that translate EEG data into a
                 new vectorial space (coordinates of this space being
                 the new attributes), where projected data can be more
                 easily classified. Although our method is applied here
                 in a straightforward way to check for feasibility, it
                 has achieved reasonable classification results that are
                 comparable to those obtained by other state of the art
                 algorithms. In the future, we expect that by choosing
                 carefully primitive functions, Genetic Programming will
                 be able to give original results that cannot be matched
                 by other machine learning classification algorithms.",

Genetic Programming entries for Cesar Estebanez Jose Maria Valls Ferran Ricardo Aler Mur Ines Maria Galvan Leon