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A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data

A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data

Vandana Roy, Shailja Shukla
Copyright: © 2017 |Volume: 29 |Issue: 4 |Pages: 19
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781522510871|DOI: 10.4018/JOEUC.2017100105
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MLA

Roy, Vandana, and Shailja Shukla. "A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data." JOEUC vol.29, no.4 2017: pp.84-102. http://doi.org/10.4018/JOEUC.2017100105

APA

Roy, V. & Shukla, S. (2017). A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data. Journal of Organizational and End User Computing (JOEUC), 29(4), 84-102. http://doi.org/10.4018/JOEUC.2017100105

Chicago

Roy, Vandana, and Shailja Shukla. "A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data," Journal of Organizational and End User Computing (JOEUC) 29, no.4: 84-102. http://doi.org/10.4018/JOEUC.2017100105

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Abstract

The Big data as Electroencephalography (EEG) can induce by artifacts during acquisition process which will obstruct the features and quality of interest in the signal. The healthcare diagnostic procedures need strong and viable biomedical signals and elimination of artifacts from EEG is important. In this research paper, an improved ensemble approach is proposed for single channel EEG signal motion artifacts removal. Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis (EEMD-CCA) filter combination are applied to remove artifact effectively and further Stationary Wavelet Transform (SWT) is applied to remove the randomness and unpredictability due to motion artifacts from EEG signals. This new filter combination technique was tested against currently available artifact removal techniques and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use.

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