International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 6, No. 7, 8 Jul. 2014
Cover page and Table of Contents: PDF (size: 736KB)
Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet
Full Text (PDF, 736KB), PP.31-39
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Author(s)
Vandana Roy
1,*
Shailja Shukla
2
Index Terms
Artifacts removal, Biomedical Signal Filtering, Electroencephalogram (EEG), source separation, Spatially-Constrained Independent Component Analysis (SCICA), thresholding, daubechies wavelet
Abstract
This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG) signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the deflation approach Spatially-Constrained Independent Component Analysis (SCICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.
Cite This Paper
Vandana Roy, Shailja Shukla, "Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet", International Journal of Modern Education and Computer Science (IJMECS), vol.6, no.7, pp.31-39, 2014. DOI:10.5815/ijmecs.2014.07.05
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