Removing noise from event-related potentials using a probabilistic generative model with grouped covariance matrices
Hayato Maki, Tomoki Toda, Sakriani Sakti, Graham Neubig, Satoshi Nakamura · 2016
Analysis of electroencephalograms (EEG) usually suffers from a variety of noises. In this paper, we propose a new method for background noise removal from single-trial event-related potentials (ERPs) recorded with a multi-channel EEG. An observed signal is separated into multiple signals with a multi-channel Wiener filter, whose coefficients are estimated based on a probabilistic generative model in the time-frequency domain. The main contribution is a method to estimate covariance matrices for each frequency bins of short-time Fourier transform (STFT) representing different spatial spread of a multi-channel EEG signal according to frequencies. An experiment using a pseudo-ERP data set demonstrates the effectiveness of our proposed method.