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Artifact Removal from EEG using Spatially Constrained FastICA and Fuzzy Shrink Thresholding Technique
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 4 - Number 11 |
| Year of Publication: 2012 |
| Authors: G. Geetha, S. N. Geethalakshmi |
10.5120/ijais12-450814
|
G. Geetha, S. N. Geethalakshmi . Artifact Removal from EEG using Spatially Constrained FastICA and Fuzzy Shrink Thresholding Technique. International Journal of Applied Information Systems. 4, 11 ( December 2012), 25-29. DOI=10.5120/ijais12-450814
Abstract
This paper presents a novel technique for removing the artifacts from the Electroencephalogram (EEG) signals. EEG signals are influenced by different characteristics, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The elimination of artifact from scalp EEGs is of substantial significance for both the automated and visual examination of underlying brainwave actions. These noise sources increase the difficulty in analysing the EEG and obtaining clinical information related to pathology. Hence it is crucial to design a procedure to decrease such artifacts in EEG records. This paper uses Spatially-Constrained Fast ICA (SC-FastICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next step, Wavelet Denoising (WD) is applied to extract the brain activity from purged artifacts, where thresholding plays an important role in delineating the artifacts and hence a better thresholding technique called fuzzy Shrink thresholding is applied. Experimental results show that the proposed technique results in better removal of artifacts.
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