Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm

Shoker, L.; Sanei, Saeid; Chambers, J.A.

doi:10.1109/lsp.2005.855539

Cited by 140 publications

(100 citation statements)

References 6 publications

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“…Approaches such as trial rejection, eye fixation, EOG subtraction, principal component analysis (PCA) [3], blind source separation (BSS) using ICA [4]- [6], spatial [7], and H ∞ [8] adaptive filters have also been documented as having varying success. Despite no quantitative comparison for any reference dataset being available, it has been shown that the regression-and BSS-based methods are the most reliable ones [1], [2], [4]- [6].…”

Section: Removal Of the Eye-blink Artifacts From Eegs Via Stf-ts Modementioning

confidence: 99%

“…The major advantage of the proposed method is that unlike the respective regression-and BSS-based methods presented in [2] and [4], it needs neither the reference EOG channel recordings nor any objective criterion for distinguishing between EB and spurious peaks in the ongoing EEGs. Reducing the computational complexity in the estimation of the STF model using the PARAFAC is achievable by subdividing the time domain into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC.…”

Section: Removal Of the Eye-blink Artifacts From Eegs Via Stf-ts Modementioning

confidence: 99%

“…The sources are presumed to be uncorrelated, 4 therefore, the time-lagged-symmetrized autocorrelation matrix R k xx can be calculated as …”

Section: Algorithm Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

Nazarpour¹,

Wongsawat

Sanei

et al. 2008

IEEE Trans. Biomed. Eng.

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Section: Removal Of the Eye-blink Artifacts From Eegs Via Stf-ts Modementioning

confidence: 99%

Section: Removal Of the Eye-blink Artifacts From Eegs Via Stf-ts Modementioning

confidence: 99%

See 1 more Smart Citation

Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

Nazarpour¹,

Wongsawat

Sanei

et al. 2008

IEEE Trans. Biomed. Eng.

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“…It is well known that the enormous indeterminacies in brain make the BSS problem ill-posed; however, statistical natures lead to restoring the well-posedness of the problem in a biosignal processing. By the properties, theoretically multivariate statistical analysis approaches like independent component analysis (ICA) can separate observed EEG signals into spatially and temporally distinguishable components effectively, and then, estimated components will be identified as neuronal or artifactual sources by hard/soft threshold to reconstruct artifact-free EEG matrix [10,11]. Whereas there are several reviews on artifact rejection methods including overall procedure (signal separation, component identification, and signal reconstruction) for multi-channel EEG signals [12][13][14][15][16], we have never seen review of artifact rejection methods for single-channel EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Review of Artifact Rejection Methods for Electroencephalographic Systems

Kanoga¹,

Mitsukura²

2017

Electroencephalography

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Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms.

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“…Approaches such as trial rejection, eye fixation, EOG subtraction, principal component analysis (PCA) [3], blind source separation (BSS) using ICA [4], [5], and H ∞ [6] adaptive filters have also been documented as having varying success. Despite no quantitative comparison for any reference dataset being available, it has been shown that the regression-and BSS-based methods are the most reliable ones [1], [2], [4], and [5]. Although beamforming-based methods have been recently utilized in the EEG source localization problem [7], to the authors' best knowledge, they have not been considered in removing the EB artifacts from the EEGs.…”

Section: Introductionmentioning

confidence: 99%

A Robust Minimum Variance Beamforming Approach for the Removal of the Eye-Blink Artifacts from EEGs

Nazarpour

Wongsawat

Sanei

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This item was submitted to Loughborough's Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ A Robust Minimum Variance Beamforming Approach for the Removal of the Eye-Blink Artifacts from EEGsKianoush Nazarpour, Yodchanan Wongsawat, Saeid Sanei, Soontorn Oraintara, and Jonathon A. ChambersAbstract-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.

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Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm

Cited by 140 publications

References 6 publications

Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

Removal of the Eye-Blink Artifacts From EEGs via STF-TS Modeling and Robust Minimum Variance Beamforming

Review of Artifact Rejection Methods for Electroencephalographic Systems

A Robust Minimum Variance Beamforming Approach for the Removal of the Eye-Blink Artifacts from EEGs

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