Key result
Novel ensemble decomposition-regression method suppresses cardiac artifacts with less EEG distortion than conventional techniques.
Why the study?
Artifact signals from eye blinks and cardiac activity contaminate EEG recordings, complicating data analysis and requiring effective suppression methods.
Does a methodology combining ensemble empirical mode decomposition with regression reduce signal distortion compared to conventional regression or wavelet-based approaches in EEG recordings?
Does a methodology combining ensemble empirical mode decomposition with regression reduce signal distortion compared to conventional regression or wavelet-based approaches in EEG recordings?
The proposed methodology effectively suppresses both cardiac and ocular artifacts in EEG recordings with less signal distortion than conventional methods.
May aid EEG interpretation in neurological disorders; leaves open prospective validation before clinical use.
Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording. In the proposed methodology, the clean EOG and EKG signals are extracted by subjecting the raw reference time-series data to ensemble empirical mode decomposition to obtain the intrinsic mode functions. Then, an unsupervised technique is used to capture the artifact components. We compare the distortion introduced into the brain signal after artifact suppression using the proposed method with those obtained using conventional regression alone and with a wavelet-based approach. The results show that the proposed method outperforms the other techniques, with an additional advantage of being a common methodology for the suppression of two types of artifact.
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Patel et al. (2017) studied EEG artifacts (n=4). Ensemble Empirical Mode Decomposition (EEMD) combined with regression vs. Conventional regression alone and wavelet-based approach was evaluated on Distortion introduced in the brain signal after artifact suppression, measured by change in power spectral density (ΔPSD). Combining ensemble empirical mode decomposition with a regression approach effectively suppressed ocular and cardiac artifacts from EEG data with lower distortion than conventional techniques.
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