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Title: A hybrid algorithm for removal of eye blinking artifacts from electroencephalograms
Authors: Shoker, Leor
Sanei, Saeid
Chambers, Jonathon
Keywords: Eye blinking artifact
Blind source separation
Support vector machines
Electroencephalogram and electroculogram
Issue Date: 2005
Publisher: © IEEE
Citation: SHOKER, L., SANEI, S. and CHAMBERS, J.A., 2005. A hybrid algorithm for removal of eye blinking artifacts from electroencephalograms. IN: 2005 13th Workshop on Statistical Signal Processing (IEEE/SP 2005), Bordeaux, France, 17-20 July, pp. 1014-1017.
Abstract: A robust method for removal of artifacts such as eye blinks and electrocardiogram (ECG) from the electroencephalograms (EEGs) has been developed in this paper. The proposed hybrid method fuses support vector machines (SVMs) based classification and blind source separation (BSS) based on independent component analysis (ICA). The carefully chosen features for the classifier mainly represent the data higher order statistics. 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 results from the simulation studies show that the algorithm outperforms previously proposed algorithms.
Description: This is a conference paper [© IEEE]. It 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
URI: https://dspace.lboro.ac.uk/2134/5614
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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