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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
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/06/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Human Cognition and Behavioral Science and Technology)
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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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.