Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG

R. Palaniappan; P. Raveendran

doi:10.1109/APCCAS.2002.1115119

Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG

R. Palaniappan, P. Raveendran

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

8 Citations (Scopus)

Abstract

Proposes a technique that uses genetic algorithm (GA) to select optimal features for classification applications using fuzzy ARTMAP (FA) neural network (NN). The technique is applied to select features for classification of two groups of subjects: alcoholics and controls, using multi-channel single trial electroencephalogram (EEG) signals evoked during visual response. The results show that the proposed technique is successful in selecting the features that contribute towards classification. This serves to reduce the number of required features while improving the classification performance. The results also indicate that the gamma band spectral power could be used to support evidence on the residual effects of long-term use of alcohol on visual response.

Original language	English
Title of host publication	Proceedings - APCCAS 2002
Subtitle of host publication	Asia-Pacific Conference on Circuits and Systems
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	53-56
Number of pages	4
ISBN (Electronic)	0780376900
DOIs	https://doi.org/10.1109/APCCAS.2002.1115119
Publication status	Published - 2002
Externally published	Yes
Event	IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002 - Denpasar, Bali, Indonesia Duration: 28 Oct 2002 → 31 Oct 2002 https://ieeexplore.ieee.org/xpl/conhome/8182/proceeding (Proceedings)

Publication series

Name	IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS
Volume	2

Conference

Conference	IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002
Abbreviated title	APCCAS 2002
Country/Territory	Indonesia
City	Denpasar, Bali
Period	28/10/02 → 31/10/02
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8182/proceeding (Proceedings)

Keywords

Alcoholism
Convergence
Electroencephalography
Feature extraction
Fuzzy neural networks
Genetic algorithms
Genetic mutations
Information science
Neural networks
Testing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/APCCAS.2002.1115119

Cite this

Palaniappan, R., & Raveendran, P. (2002). Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG. In Proceedings - APCCAS 2002: Asia-Pacific Conference on Circuits and Systems (pp. 53-56). (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS; Vol. 2). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/APCCAS.2002.1115119

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title = "Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG",

abstract = "Proposes a technique that uses genetic algorithm (GA) to select optimal features for classification applications using fuzzy ARTMAP (FA) neural network (NN). The technique is applied to select features for classification of two groups of subjects: alcoholics and controls, using multi-channel single trial electroencephalogram (EEG) signals evoked during visual response. The results show that the proposed technique is successful in selecting the features that contribute towards classification. This serves to reduce the number of required features while improving the classification performance. The results also indicate that the gamma band spectral power could be used to support evidence on the residual effects of long-term use of alcohol on visual response.",

keywords = "Alcoholism, Convergence, Electroencephalography, Feature extraction, Fuzzy neural networks, Genetic algorithms, Genetic mutations, Information science, Neural networks, Testing",

author = "R. Palaniappan and P. Raveendran",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002, APCCAS 2002 ; Conference date: 28-10-2002 Through 31-10-2002",

year = "2002",

doi = "10.1109/APCCAS.2002.1115119",

language = "English",

series = "IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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booktitle = "Proceedings - APCCAS 2002",

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Palaniappan, R & Raveendran, P 2002, Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG. in Proceedings - APCCAS 2002: Asia-Pacific Conference on Circuits and Systems. IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS, vol. 2, IEEE, Institute of Electrical and Electronics Engineers, pp. 53-56, IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002, Denpasar, Bali, Indonesia, 28/10/02. https://doi.org/10.1109/APCCAS.2002.1115119

Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG. / Palaniappan, R.; Raveendran, P.
Proceedings - APCCAS 2002: Asia-Pacific Conference on Circuits and Systems. IEEE, Institute of Electrical and Electronics Engineers, 2002. p. 53-56 (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG

AU - Palaniappan, R.

AU - Raveendran, P.

PY - 2002

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N2 - Proposes a technique that uses genetic algorithm (GA) to select optimal features for classification applications using fuzzy ARTMAP (FA) neural network (NN). The technique is applied to select features for classification of two groups of subjects: alcoholics and controls, using multi-channel single trial electroencephalogram (EEG) signals evoked during visual response. The results show that the proposed technique is successful in selecting the features that contribute towards classification. This serves to reduce the number of required features while improving the classification performance. The results also indicate that the gamma band spectral power could be used to support evidence on the residual effects of long-term use of alcohol on visual response.

AB - Proposes a technique that uses genetic algorithm (GA) to select optimal features for classification applications using fuzzy ARTMAP (FA) neural network (NN). The technique is applied to select features for classification of two groups of subjects: alcoholics and controls, using multi-channel single trial electroencephalogram (EEG) signals evoked during visual response. The results show that the proposed technique is successful in selecting the features that contribute towards classification. This serves to reduce the number of required features while improving the classification performance. The results also indicate that the gamma band spectral power could be used to support evidence on the residual effects of long-term use of alcohol on visual response.

KW - Alcoholism

KW - Convergence

KW - Electroencephalography

KW - Feature extraction

KW - Fuzzy neural networks

KW - Genetic algorithms

KW - Genetic mutations

KW - Information science

KW - Neural networks

KW - Testing

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M3 - Conference Paper

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T3 - IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

SP - 53

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BT - Proceedings - APCCAS 2002

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Palaniappan R, Raveendran P. Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG. In Proceedings - APCCAS 2002: Asia-Pacific Conference on Circuits and Systems. IEEE, Institute of Electrical and Electronics Engineers. 2002. p. 53-56. (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS). doi: 10.1109/APCCAS.2002.1115119

Genetic algorithm to select features for fuzzy ARTMAP classification of evoked EEG

Abstract

Publication series

Conference

Keywords

UN SDGs

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