Enhancing Event-Related Potentials Based on Maximum a Posteriori Estimation with a Spatial Correlation Prior

Hayato MAKI  Tomoki TODA  Sakriani SAKTI  Graham NEUBIG  Satoshi NAKAMURA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.6   pp.1437-1446
Publication Date: 2016/06/01
Publicized: 2016/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015CBP0008
Type of Manuscript: Special Section PAPER (Special Section on Human Cognition and Behavioral Science and Technology)
Category: 
Keyword: 
electroencephalogram (EEG),  event-related potential (ERP),  generative model,  independent component analysis (ICA),  Wiener filter,  noise removal,  Wishart distribution,  spatial correlation prior,  

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Summary: 
In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.