High-significance Averages of Event-Related Potential via Genetic Programming

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

  author =       "Luca Citi and Riccardo Poli and Caterina Cinel",
  title =        "High-significance Averages of Event-Related Potential
                 via Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice {VII}",
  year =         "2009",
  editor =       "Rick L. Riolo and Una-May O'Reilly and 
                 Trent McConaghy",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor",
  month =        "14-16 " # may,
  publisher =    "Springer",
  chapter =      "9",
  pages =        "135--157",
  keywords =     "genetic algorithms, genetic programming, Event-related
                 potentials, Register-based GP, Memory-with-Memory",
  isbn13 =       "978-1-4419-1653-2",
  DOI =          "doi:10.1007/978-1-4419-1626-6_9",
  abstract =     "In this paper we use register-based genetic
                 programming with memory-with memory to discover
                 probabilistic membership functions that are used to
                 divide up data-sets of event-related potentials
                 recorded via EEG in psycho-physiological experiments
                 based on the corresponding response times. The
                 objective is to evolve membership functions which lead
                 to maximising the statistical significance with which
                 true brain waves can be reconstructed when averaging
                 the trials in each bin. Results show that GP can
                 significantly improve the fidelity with which ERP
                 components can be recovered.",
  notes =        "part of \cite{Riolo:2009:GPTP}",

Genetic Programming entries for Luca Citi Riccardo Poli Caterina Cinel