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

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

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

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

Bristol Medical School (PHS)

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

182 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 language	English
Title of host publication	WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)
Place of Publication	NEW YORK
Publisher	Association for Computing Machinery
Pages	177-183
Number of pages	7
ISBN (Print)	978-1-60558-326-6
Publication status	Published - 2009
Event	World Summit on Genetic and Evolutionary Computation (GEC 09) - Shanghai, China Duration: 12 Jun 2009 → 14 Jun 2009

Conference

Conference	World Summit on Genetic and Evolutionary Computation (GEC 09)
Country/Territory	China
City	Shanghai
Period	12/06/09 → 14/06/09

Handle.net

Persistent link

Cite this

@inproceedings{e177438bed9d4581aa1071fb8067ce24,

title = "Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks",

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.",

author = "Ling Guo and Daniel Rivero and Seoane, \{Jose A.\} and Alejandro Pazos",

year = "2009",

language = "English",

isbn = "978-1-60558-326-6",

pages = "177--183",

booktitle = "WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)",

publisher = "Association for Computing Machinery",

address = "United States",

note = "World Summit on Genetic and Evolutionary Computation (GEC 09) ; Conference date: 12-06-2009 Through 14-06-2009",

}

Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks. / Guo, Ling; Rivero, Daniel; Seoane, Jose A. et al.
WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09). NEW YORK: Association for Computing Machinery, 2009. p. 177-183.

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

TY - GEN

T1 - Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks

AU - Guo, Ling

AU - Rivero, Daniel

AU - Seoane, Jose A.

AU - Pazos, Alejandro

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-60558-326-6

SP - 177

EP - 183

BT - WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)

PB - Association for Computing Machinery

CY - NEW YORK

T2 - World Summit on Genetic and Evolutionary Computation (GEC 09)

Y2 - 12 June 2009 through 14 June 2009

ER -

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

Abstract

Conference

Handle.net

Fingerprint

Cite this