Learning to recognise mental activities: genetic programming of stateful classifiers for brain-computer interfacing

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Alexandros Agapitos and Matthew Dyson and 
                 Simon M. Lucas and Francisco Sepulveda",
  title =        "Learning to recognise mental activities: genetic
                 programming of stateful classifiers for brain-computer
  booktitle =    "GECCO '08: Proceedings of the 10th annual conference
                 on Genetic and evolutionary computation",
  year =         "2008",
  editor =       "Maarten Keijzer and Giuliano Antoniol and 
                 Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and 
                 Nikolaus Hansen and John H. Holmes and 
                 Gregory S. Hornby and Daniel Howard and James Kennedy and 
                 Sanjeev Kumar and Fernando G. Lobo and 
                 Julian Francis Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Jordan Pollack and Kumara Sastry and 
                 Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and 
                 Ingo Wegener",
  isbn13 =       "978-1-60558-130-9",
  pages =        "1155--1162",
  address =      "Atlanta, GA, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1155.pdf",
  DOI =          "doi:10.1145/1389095.1389326",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, Brain
                 computer interface, classification on Raw signal,
                 stateful representation, statistical signal
  size =         "8 pages",
  abstract =     "Two families (stateful and stateless) of genetically
                 programmed classifiers were tested on a five class
                 brain-computer interface (BCI) data set of raw EEG
                 signals. The ability of evolved classifiers to
                 discriminate mental tasks from each other were analysed
                 in terms of accuracy, precision and recall. A model
                 describing the dynamics of state usage in stateful
                 programs is introduced. An investigation of
                 relationships between the model attributes and
                 associated classification results was made. The results
                 show that both stateful and stateless programs can be
                 successfully evolved for this task, though stateful
                 programs start from lower fitness and take longer to
  notes =        "GECCO-2008 A joint meeting of the seventeenth
                 international conference on genetic algorithms
                 (ICGA-2008) and the thirteenth annual genetic
                 programming conference (GP-2008).

                 ACM Order Number 910081. Also known as \cite{1389326}",

Genetic Programming entries for Alexandros Agapitos Matthew Dyson Simon M Lucas Francisco Sepulveda