Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks

Ling Guo, Daniel Rivero, Jose A. Seoane, Alejandro Pazos

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

130 Citations (Scopus)

Abstract

Electroencephalographms (EEGs) are records of brain electrical activity. It is an indispensable tool for diagnosing neurological diseases, such as epilepsy. Wavelet transform (WT) is an effective tool for analysis of non-stationary signal, such as EEGs. Relative wavelet energy (RWE) provides information about the relative energy associated with different frequency bands present in EEG signals and their corresponding degree of importance. This paper deals with a novel method of analysis of EEG signals using relative wavelet energy, and classification using Artificial Neural Networks (ANNs). The obtained classification accuracy confirms that the proposed scheme has potential in classifying EEG signals.

Original languageEnglish
Title of host publicationWORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)
Place of PublicationNEW YORK
PublisherAssociation for Computing Machinery (ACM)
Pages177-183
Number of pages7
ISBN (Print)978-1-60558-326-6
Publication statusPublished - 2009
EventWorld Summit on Genetic and Evolutionary Computation (GEC 09) - Shanghai, China
Duration: 12 Jun 200914 Jun 2009

Conference

ConferenceWorld Summit on Genetic and Evolutionary Computation (GEC 09)
CountryChina
CityShanghai
Period12/06/0914/06/09

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