Classification of EEG signals using a novel genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Arpit Bhardwaj and Aruna Tiwari and 
                 M. Vishaal Varma and M. Ramesh Krishna",
  title =        "Classification of EEG signals using a novel genetic
                 programming approach",
  booktitle =    "GECCO 2014 Workshop on Medical Applications of Genetic
                 and Evolutionary Computation (MedGEC)",
  year =         "2014",
  editor =       "Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1297--1304",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2598394.2609851",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper, we present a new method for
                 classification of electroencephalogram (EEG) signals
                 using Genetic Programming (GP). The Empirical Mode
                 Decomposition (EMD) is used to extract the features of
                 EEG signals which served as an input for the GP. In
                 this paper, new constructive crossover and mutation
                 operations are also produced to improve GP. In these
                 constructive crossover and mutation operators hill
                 climbing search is integrated to remove the destructive
                 nature of these operators. To improve GP, we apply
                 constructive crossover on all the individuals which
                 remain after reproduction. A new concept of selecting
                 the global prime off-springs of the generation is also
                 proposed. The constructive mutation approach is applied
                 to poor individuals who are left after selecting
                 globally prime off-springs. Improvement of the method
                 is measured against classification accuracy, training
                 time and the number of generations for EEG signal
                 classification. As we show in the results section, the
                 classification accuracy can be estimated to be
                 98.69percent on the test cases, which is better than
                 classification accuracy of Liang and coworkers method
                 which was published in 2010.",
  notes =        "Also known as \cite{2609851} Distributed at

Genetic Programming entries for Arpit Bhardwaj Aruna Tiwari M Vishaal Varma M Ramesh Krishna