A First Attempt at Constructing Genetic Programming Expressions for EEG Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{DBLP:conf/icann/EstebanezVAG05,

• 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",
• 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

Citations