IEICE TRANSACTIONS on transactions
Automatic EEG Classification Based of Syntactical Pattern Recognition Method--Feature Extraction by Adaptive ARMA Model Fitting--
Summary :
This paper shows a novel automatic classification method for the electroencephalogram (EEG) based on syntactical pattern recognition. The syntactical method is effective to represent the complicated structure of the features of the EEG which contains transient-waves as well as the background-wave. For the extraction of transient-waves an adaptive autoregressive-moving average (ARMA) model fitting is utilized where the input to the model is replaced by the modified input if the prediction error grows more than a given threshold. By the adaptive ARMA model transient-waves and the spectrum of the background-wave are obtained from the prediction error and ARMA parameters, respectively. Since transient-waves may contain noisy patterns or variances, a relaxation scheme is applied. As the second stage all of the features of the EEG including the spectrum are described syntactically according to the generative grammar. Then the syntactical description Ti inherent to the diagnosis is obtained. In order to reduce the ambiguity and to suppress the complexity of syntactical descriptions, numerical values representing the details of EEG are separated from the syntactical description, and are added as the attributes (this method is generally called the attributed grammar). For the input EEG having syntactical description Ti, the final diagnostic decision is made by using the statistical Bayes estimation about the attributes within the group for Ti. As the result of automatic EEG classification for 200 EEG samples correct recognition of about 80 percent is observed.
- Publication
- IEICE TRANSACTIONS on transactions Vol.E69-E No.10 pp.1125-1132
- Publication Date
- 1986/10/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Medical Electronics and Bioengineering
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Shozo TOKINAGA, "Automatic EEG Classification Based of Syntactical Pattern Recognition Method--Feature Extraction by Adaptive ARMA Model Fitting--" in IEICE TRANSACTIONS on transactions,
vol. E69-E, no. 10, pp. 1125-1132, October 1986, doi: .
Abstract: This paper shows a novel automatic classification method for the electroencephalogram (EEG) based on syntactical pattern recognition. The syntactical method is effective to represent the complicated structure of the features of the EEG which contains transient-waves as well as the background-wave. For the extraction of transient-waves an adaptive autoregressive-moving average (ARMA) model fitting is utilized where the input to the model is replaced by the modified input if the prediction error grows more than a given threshold. By the adaptive ARMA model transient-waves and the spectrum of the background-wave are obtained from the prediction error and ARMA parameters, respectively. Since transient-waves may contain noisy patterns or variances, a relaxation scheme is applied. As the second stage all of the features of the EEG including the spectrum are described syntactically according to the generative grammar. Then the syntactical description Ti inherent to the diagnosis is obtained. In order to reduce the ambiguity and to suppress the complexity of syntactical descriptions, numerical values representing the details of EEG are separated from the syntactical description, and are added as the attributes (this method is generally called the attributed grammar). For the input EEG having syntactical description Ti, the final diagnostic decision is made by using the statistical Bayes estimation about the attributes within the group for Ti. As the result of automatic EEG classification for 200 EEG samples correct recognition of about 80 percent is observed.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e69-e_10_1125/_p
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@ARTICLE{e69-e_10_1125,
author={Shozo TOKINAGA, },
journal={IEICE TRANSACTIONS on transactions},
title={Automatic EEG Classification Based of Syntactical Pattern Recognition Method--Feature Extraction by Adaptive ARMA Model Fitting--},
year={1986},
volume={E69-E},
number={10},
pages={1125-1132},
abstract={This paper shows a novel automatic classification method for the electroencephalogram (EEG) based on syntactical pattern recognition. The syntactical method is effective to represent the complicated structure of the features of the EEG which contains transient-waves as well as the background-wave. For the extraction of transient-waves an adaptive autoregressive-moving average (ARMA) model fitting is utilized where the input to the model is replaced by the modified input if the prediction error grows more than a given threshold. By the adaptive ARMA model transient-waves and the spectrum of the background-wave are obtained from the prediction error and ARMA parameters, respectively. Since transient-waves may contain noisy patterns or variances, a relaxation scheme is applied. As the second stage all of the features of the EEG including the spectrum are described syntactically according to the generative grammar. Then the syntactical description Ti inherent to the diagnosis is obtained. In order to reduce the ambiguity and to suppress the complexity of syntactical descriptions, numerical values representing the details of EEG are separated from the syntactical description, and are added as the attributes (this method is generally called the attributed grammar). For the input EEG having syntactical description Ti, the final diagnostic decision is made by using the statistical Bayes estimation about the attributes within the group for Ti. As the result of automatic EEG classification for 200 EEG samples correct recognition of about 80 percent is observed.},
keywords={},
doi={},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - Automatic EEG Classification Based of Syntactical Pattern Recognition Method--Feature Extraction by Adaptive ARMA Model Fitting--
T2 - IEICE TRANSACTIONS on transactions
SP - 1125
EP - 1132
AU - Shozo TOKINAGA
PY - 1986
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E69-E
IS - 10
JA - IEICE TRANSACTIONS on transactions
Y1 - October 1986
AB - This paper shows a novel automatic classification method for the electroencephalogram (EEG) based on syntactical pattern recognition. The syntactical method is effective to represent the complicated structure of the features of the EEG which contains transient-waves as well as the background-wave. For the extraction of transient-waves an adaptive autoregressive-moving average (ARMA) model fitting is utilized where the input to the model is replaced by the modified input if the prediction error grows more than a given threshold. By the adaptive ARMA model transient-waves and the spectrum of the background-wave are obtained from the prediction error and ARMA parameters, respectively. Since transient-waves may contain noisy patterns or variances, a relaxation scheme is applied. As the second stage all of the features of the EEG including the spectrum are described syntactically according to the generative grammar. Then the syntactical description Ti inherent to the diagnosis is obtained. In order to reduce the ambiguity and to suppress the complexity of syntactical descriptions, numerical values representing the details of EEG are separated from the syntactical description, and are added as the attributes (this method is generally called the attributed grammar). For the input EEG having syntactical description Ti, the final diagnostic decision is made by using the statistical Bayes estimation about the attributes within the group for Ti. As the result of automatic EEG classification for 200 EEG samples correct recognition of about 80 percent is observed.
ER -
English
