Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

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概要

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• 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.

著者

• YANG Hyung-Jeong

  Chonnam National University

• NG Kam

  Chonnam National University

• KIM Soo-Hyung

  Chonnam National University

• KIM Sun-Hee

  Chonnam National University

関連論文

• Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data
• Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

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