IEICE TRANSACTIONS on Information
Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data
Summary :
In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.12 pp.3010-3016
- Publication Date
- 2012/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.3010
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
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Kam Swee NG, Hyung-Jeong YANG, Soo-Hyung KIM, Sun-Hee KIM, "Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 12, pp. 3010-3016, December 2012, doi: 10.1587/transinf.E95.D.3010.
Abstract: In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.3010/_p
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@ARTICLE{e95-d_12_3010,
author={Kam Swee NG, Hyung-Jeong YANG, Soo-Hyung KIM, Sun-Hee KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data},
year={2012},
volume={E95-D},
number={12},
pages={3010-3016},
abstract={In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.},
keywords={},
doi={10.1587/transinf.E95.D.3010},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data
T2 - IEICE TRANSACTIONS on Information
SP - 3010
EP - 3016
AU - Kam Swee NG
AU - Hyung-Jeong YANG
AU - Soo-Hyung KIM
AU - Sun-Hee KIM
PY - 2012
DO - 10.1587/transinf.E95.D.3010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2012
AB - In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.
ER -
English
