Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm
Open Access
- 19 September 2005
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Letters
- Vol. 12 (10) , 721-724
- https://doi.org/10.1109/lsp.2005.855539
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.Keywords
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