title: A robust minimum variance beamforming approach for the removal of the eye-blink artifacts from EEGs.
abstract: 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.
authors.affiliation: Centre of Digital Signal Processing, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK. NazarpourK@cf.ac.uk
authors.forename: Kianoush
authors.initials: K
authors.lastname: Nazarpour
authors.forename: Yodchanan
authors.initials: Y
authors.lastname: Wongsawat
authors.forename: Saeid
authors.initials: S
authors.lastname: Sanei
authors.forename: Soontorn
authors.initials: S
authors.lastname: Oraintara
authors.forename: Jonathon A
authors.initials: JA
authors.lastname: Chambers
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D001767:Blinking / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity