Classification of EEG signals using a novel genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Bhardwaj:2014:GECCOcomp,

• 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 = "http://doi.acm.org/10.1145/2598394.2609851",
• 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 GECCO-2014.",

}

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

Citations