title: Removing noise from event-related potentials using a probabilistic generative model with grouped covariance matrices.
abstract: 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.
authors.forename: Hayato
authors.initials: H
authors.lastname: Maki
authors.forename: Tomoki
authors.initials: T
authors.lastname: Toda
authors.forename: Sakriani
authors.initials: S
authors.lastname: Sakti
authors.forename: Graham
authors.initials: G
authors.lastname: Neubig
authors.forename: Satoshi
authors.initials: S
authors.lastname: Nakamura
mesh_terms: D000328:Adult; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted