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Title: Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm
Authors: Shoker, Leor
Sanei, Saeid
Chambers, Jonathon
Keywords: Artifact removal
Blind source separation bss
Electroencephalogram eeg
Support vector machines svms
Issue Date: 2005
Publisher: © IEEE
Citation: SHOKER, L., SANEI, S. and CHAMBERS, J., 2005. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Processing Letters, 12 (10), pp. 721-724.
Abstract: Artifacts such as eye blinks and heart rhythm (ECG) cause the main interfering signals within electroencephalogram (EEG) measurements. Therefore, we propose a method for artifact removal based on exploitation of certain carefully chosen statistical features of independent components extracted from the EEGs, by fusing support vector machines (SVMs) and blind source separation (BSS). We use the second-order blind identification (SOBI) algorithm to separate the EEG into statistically independent sources and SVMs to identify the artifact components and thereby to remove such signals. The remaining independent components are remixed to reproduce the artifact-free EEGs. Objective and subjective assessment of the simulation results shows that the algorithm is successful in mitigating the interference within EEGs.
Description: This article was published in the journal IEEE Signal Processing Letters [© IEEE] and is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/LSP.2005.855539
URI: https://dspace.lboro.ac.uk/2134/5650
ISSN: 1070-9908
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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