Title :
Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm
Author :
Shoker, Leor ; Sanei, Saeid ; Chambers, Jonathon
Author_Institution :
Centre of Digital Signal Process., Cardiff Univ., UK
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 :
blind source separation; electro-oculography; electrocardiography; electroencephalography; feature extraction; independent component analysis; interference (signal); medical signal processing; support vector machines; ECG; EEG measurement; SOBI algorithm; artifact removal; blind source separation; electrocardiogram; electroencephalogram; electrooculogram; eye blink; heart rhythm; hybrid BSS-SVM algorithm; independent components extraction; objective assessment; second-order blind identification; signal interference; statistical feature; subjective assessment; support vector machine; Blind source separation; Brain modeling; Electrocardiography; Electroencephalography; Heart; Interference; Rhythm; Signal processing; Source separation; Support vector machines; Artifact removal; blind source separation (BSS); electroencephalogram (EEG); electrooculogram; support vector machines (SVMs);
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2005.855539
