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 · 2016
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.