EEG signal enhancement using multi-channel wiener filter with a spatial correlation prior

Abstract

Event-related potentials (ERPs) of electroencephalogram (EEG) are often used as features for brain machine interfaces or for analysis of brain activities. However, as EEG signals easily suffer from various artifacts, ERPs are often collapsed and hard to observe. There are several attempts at using multi-channel EEG signals to enhance EEG signals of interest and make ERPs more clearly observed. For example, a previous work has proposed a blind EEG signal separation method using a multi-channel Wiener filter designed with a probabilistic generative model of observed EEG signals. This method copes with the under-determination of EEG signal separation by assuming sparseness of each EEG component in the time-frequency domain. Although this method blindly separates EEG signals into individual EEG components using time-varying scaled spatial correlation matrices, target EEG components, such as P300 of ERP, are often known in advance in some applications. In this paper, inspired by this previous work, we propose a probabilistic EEG signal enhancement method using a multi-channel Wiener filter, newly incorporating prior information of the spatial correlation matrices related to the target EEG component in the probabilistic generative model to improve performance of EEG signal enhancement. An experimental evaluation for P300 enhancement shows that the proposed method significantly reduces artifacts.

Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

“…The frequency of brain waves differs based on the behaviour and mental state of human brain. Accordingly, the EEG signals are categorised into the following five frequency bands: delta (0.5-4 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta and gamma .…”

Single channel approach for filtering EEG signals strongly contaminated with facial EMG

“…Independent Component Analysis -ICA and Canonical Correlation Analysis -CCA). The following methods are commonly employed for this purpose: adaptive filtering [19,20]; wiener filtering [21,22], Bayesian filtering [23], Blind Source Separation (BSS) [24,25], wavelet transform (WT) [26,27], Empirical Mode Decomposition (EMD) [28,29] and the combination of these techniques (i.e., hybrid methods) [14,[30][31][32][33].…”

Support vector machine and higher‐order cumulants based blind identification for non‐linear Wiener models

“…Among different non-linear system identification structures, Wiener models have been found very useful in modelling nonlinear systems such as pH neutralisation processes [20], distillation column [21], and electroencephalograph [22]. Recently, the existing literature related to the identification of Wiener models includes the correlation analysis method [23], maximum likelihood method [24], subspace method [25], particle swarm optimisation algorithm (PSOM) [26,27], genetic algorithm (GA) [28], frequently method [29], semi-parametric Bayesian method [30], recursive identification method [31], iterative method [32], differential evolution algorithm (DEA) [33], orthonormal basis functions method [34], recursive least square method (LSM) [35], hierarchical gradient approach (HGA) [36] etc.…”