Key result
The proposed EEMD-CCA and SWT filter combination effectively removed motion artifacts from single-channel EEG signals and is suitable as a supplement to current algorithms.
A novel ensemble approach using EEMD-CCA and SWT can effectively eliminate motion artifacts from single-channel EEG data.
May enhance single-channel EEG monitoring; hypothesis-generating and should not yet change practice.
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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Roy et al. (2017) studied EEG motion artifacts. Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis (EEMD-CCA) with Stationary Wavelet Transform (SWT) vs. Currently available artifact removal techniques was evaluated on Artifact removal effectiveness. The proposed EEMD-CCA and SWT filter combination effectively removed motion artifacts from single-channel EEG signals and is suitable as a supplement to current algorithms.
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