A hybrid algorithm fusing support vector machines and blind source separation successfully mitigated eye blink and ECG interference within electroencephalogram measurements.
A hybrid BSS-SVM algorithm successfully removes artifacts, including ECG interference, from EEG measurements.
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.
Shoker et al. (Tue,) conducted a other in EEG artifacts. Hybrid BSS-SVM algorithm was evaluated on Artifact removal from EEGs. A hybrid algorithm fusing support vector machines and blind source separation successfully mitigated eye blink and ECG interference within electroencephalogram measurements.