Artifact Removal from EEG using Spatially Constrained FastICA and Fuzzy Shrink Thresholding Technique
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Date
2012
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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 o f artifact from scalp
EEGs is o f substantial significance for both the automated and
visual examination o f 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 (SCFastlCA)
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 o f artifacts.