Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm

Shoker, Leor; Sanei, Saeid; Chambers, Jonathon

Artifact Removal From Electroencephalograms Using.pdf (240.96 kB)

Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm

journal contribution

posted on 2009-12-11, 14:23 authored by Leor Shoker, Saeid Sanei, Jonathon Chambers

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.

History

School

Mechanical, Electrical and Manufacturing Engineering

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.

Publisher

Version

VoR (Version of Record)

Publication date

2005

Notes

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.