Does a hybrid signal denoising framework improve the suppression of motion artifacts in EEG signals compared to existing algorithms?
A novel hybrid signal denoising framework using modified EMD and an optimized LoG filter effectively suppresses motion artifacts in EEG signals, outperforming existing methods.
Motion artifacts are one of the most challenging non-physiological noise sources present in the biomedical signal, which can hinder the true performance of EEG-based neuro-engineering applications. Therefore, motion artifact removal from EEG signals can be potentially the utmost protuberant research topic. To solve this issue, a hybrid signal denoising framework which comprises modified empirical mode decomposition (EMD) and an optimized Laplacian of Gaussian (LoG) filter is proposed for the suppression of motion artifacts from EEG signals. The modified-EMD decomposes the single-channel noisy EEG signal into a set of the optimal number of intrinsic mode functions (IMFs). Furthermore, the optimized LoG filter has been applied to the motion artifact intermixed EEG signal which is considered as low-frequency noise likely to present at low-frequency IMFs. This filter performs smoothing of the signal and removes background noises or artifacts from the EEG signal. The denoised signal is reconstructed by adding filtered output signal and high-frequency IMFs. The robustness of the proposed method is demonstrated through simulated and real EEG data with artifacts. The experimental values of the different sets of performance assessment metrics reveal that the proposed algorithm outperforms the existing state-of-the-art EEG signal denoising algorithms for the suppression of motion artifacts from EEG signals.
Ranjan et al. (Sat,) studied this question.