Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis

Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis Electroencephalogram (EEG) signal is usually suffered from motion artifacts, generated randomly during signal acquisition timings. These artifacts sturdily affect the investigation and therefore, diagnosis of neural diseases from EEG signal. The artifact removal may cause loss of important information from the signal. Therefore, it is required to remove the motion artifacts and simultaneously preserve the desired information, which makes EEG artifact removal a vital task. Enhanced Empirical Mode Decomposition (EEMD) is the most widespread method used for artifact removal, as it is a data-driven based feature extraction method. In this research work the efficiency of various EEMD with different interpolation based artifact removal method have been compared. The EEMD is used to convert input single channel EEG signal to a multichannel signal, and in order to remove the randomness of motion artifact, CCA and DWT filtering were used successively. The performance of different interpolation based artifact removal methods have evaluated and results indicate that the proposed algorithm is suitable for use as a supplement to algorithms currently in use as it offers improvements in DSNR and various other performance parameters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis

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Springer US
Copyright © 2017 by Springer Science+Business Media, LLC
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
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