Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

Kam Swee NG  Hyung-Jeong YANG  Soo-Hyung KIM  Sun-Hee KIM  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.12   pp.3010-3016
Publication Date: 2012/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.3010
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
EEG,  independent component analysis,  principle component analysis,  incremental model,  

Full Text: PDF>>
Buy this Article

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.