Hidden pattern discovery on event related potential EEG signals

Ng, Kam Swee; Yang, Hyung-Jeong; Kim, Sun‐Hee

doi:10.1016/j.biosystems.2009.03.007

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…To check the performance of hidden variables detection, we apply our proposed method, iPCA and PCA to a real dataset. iPCA is a remarkable method for real time processing [18]. This method can summarize or can detect a few hidden variables from a large multi-dimensional dataset.…”

Section: Detection Of Hidden Variablesmentioning

confidence: 99%

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

Kim¹,

Yang²,

Kim³

et al. 2014

International Journal of Contents

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Section: Detection Of Hidden Variablesmentioning

confidence: 99%

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

Kim¹,

Yang²,

Kim³

et al. 2014

International Journal of Contents

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“…The incremental model works by processing the data at each input vector while the historical data are stored in a few variables. The previous values of the variables are updated by the next input vector [17]- [21]. This process is repeated until the end of the input data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering of Multivariate Time Series Microarray Experiments based on Incremental Non-Gaussian Analysis

Yang

Kim³

et al. 2012

International Journal of Contents

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“…The incremental learning model is, therefore, proposed as a better alternative to process the data with less memory and time consumption [8], [13], [15]. Incremental nonGaussian independent analysis has been proposed as a new feature extraction method for face recognition tasks and human hand Recognition [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

Yang

Kim

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn 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. key words : EEG, independent component analysis, principle component analysis, incremental model

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Hidden pattern discovery on event related potential EEG signals

Cited by 7 publications

References 13 publications

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

Unsupervised Clustering of Multivariate Time Series Microarray Experiments based on Incremental Non-Gaussian Analysis

Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

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