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

In this paper a new method for noise removal from singletrial 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. key words: electroencephalogram (EEG), event-related potential (ERP), generative model, independent component analysis (ICA), Wiener filter, noise removal, Wishart distribution, spatial correlation prior