A hybrid algorithm for removal of eye blinking artifacts from electroencephalograms

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...

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Bibliographic Details
Main Authors: Leor Shoker, Saeid Sanei, Jonathon Chambers
Format: Conference Proceeding
Language:English
Subjects:
Blind source separation
Electroencephalogram and electroculogram
Eye blinking artifact
Mechanical engineering not elsewhere classified
Support vector machines
Online Access:Request full text
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Summary: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.