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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.<\/jats:p>","DOI":"10.4018\/joeuc.2017100105","type":"journal-article","created":{"date-parts":[[2017,8,4]],"date-time":"2017-08-04T17:06:23Z","timestamp":1501866383000},"page":"84-102","source":"Crossref","is-referenced-by-count":58,"title":["A Methodical Healthcare Model to Eliminate Motion Artifacts from Big EEG Data"],"prefix":"10.4018","volume":"29","author":[{"given":"Vandana","family":"Roy","sequence":"first","affiliation":[{"name":"Research Scholar, Jabalpur Engineering College, Jabalpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shailja","family":"Shukla","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Jabalpur Engineering College, Jabalpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JOEUC.2017100105-0","doi-asserted-by":"publisher","DOI":"10.3390\/s151129015"},{"key":"JOEUC.2017100105-1","doi-asserted-by":"crossref","unstructured":"Anastasiadou, M. 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