A novel semi-blind signal extraction approach incorporating parafac for the removal of eye-blink artifact from EEGs

In this paper, a novel iterative blind signal extraction (BSE) scheme for the removal of the eye-blink artifact from electroencephalogram (EEC) signals is proposed. In this method, in order to remove the artifact, the signal extraction algorithm is provided with a priori information, i.e., an estimation of the column of the mixing matrix corresponding to the eye- blink source. The a priori knowledge, namely the vector corresponding to the spatial distribution of the eye-blink factor, is identified by using the method of parallel factor analysis (PARAFAC). Hence, we call the BSE approach, semi- blind signal extraction (SBSE). The results demonstrate that the proposed algorithm effectively identifies and removes the eye-blink artifact from raw EEC measurements

Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound

A novel approach for separating heart sound signals (HSSs) from lung sound recordings is presented. The approach is based on blind source extraction (BSE) with second-order statistics (SOS), which exploits the quasi-periodicity of the HSSs. The method is evaluated on both synthetic periodic signals of known period mixed with temporally white Gaussian noise (WGN) as well as on real quasi periodic HSSs mixed with lung sound signals (LSSs). Qualitative evaluation involving comparison of the power spectral densities (PSDs) of the extracted signals, by the proposed method and by the JADE algorithm, and that of the original signal is performed for the case of real data. Separation results confirm the utility of the proposed approach, although departure from strict periodicity may impact performance

A robust minimum variance beamforming approach for the removal of the eye-blink artifacts from EEGs

In this paper a novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on the robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, i.e., an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, namely the vector corresponding to the spatial distribution of the EB factor, is identified using a novel space-time-frequency-time/segment (STF-TS) model of EEGs, provided by a four-way parallel factor analysis (PARAFAC) approach. The results demonstrate that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements