An Efficient and Robust Muscle Artifact Removal Method for Few-Channel EEG

Abstract

The unavoidable muscle artifacts pose challenges on reliable interpretation of the electroencephalogram (EEG) recordings, especially for the wearable few-channel EEG, a new emerging scenario. However, the high computational load and low robustness of the existing methods limit its wider applications and performance in artifact removal. Consequently, we propose an efficient and robust muscle artifact removal approach by jointly employing the Fast Multivariate Empirical Mode Decomposition (FMEMD) and CCA for few-channel EEG. The proposed FMEMD-CCA firstly efficiently decomposes the input EEG recordings into several multivariate Intrinsic Mode Functions (IMF) by applying FMEMD. Secondly, all the multivariate IMFs are processed by CCA for computing the underlying sources. Finally, the sources with low autocorrelations are smartly determined as muscle artifacts and rejected, and therefore the other components are reconstructed for EMG-artifact-free IMFs and EEG. Simulated and real data experiments are carried out for verifying the performance of the proposed method. It takes 10 times less computing time in FMEMD-CCA compared with in Multivariate Empirical Mode Decomposition (MEMD)-CCA for 10-s EEG recordings, using the same computer and software. And the accuracy and the average correlation coefficient are highly consistent in both approaches. Furthermore, in contrast to MEMD-CCA, the proposed FMEMD-CCA works more robustly in low sampling rate based on the real data and benchmark.

An outlier detection-based method for artifact removal of few-channel EEGs

“…Previous studies applied the threshold-based methods to reject components related to muscle artifact or ocular artifact, but one major drawback of these studies is that the excessive suppression of brain activities was always ignored. As shown in figure 7 from Chen et al [21], and figure 6 from Lin et al [61], the high-frequency components were removed by threshold-based MEMD-CCA, and the suppression for the components above 10 Hz could be 40 dB. Those results only showed PSDs in the range of 0-50 Hz, and similar results were also obtained by MRA-CCA-thres, as shown in figure 5 in the current study.…”

Artifact Removal Methods in EEG Recordings: A Review

“…Compared to WT-ICA, EEMD-ICA removes artifacts effectively because EEMD is a data-driven method. FMEMD-CCA is used to remove muscle artifacts from a few channel methods with less computational time [25]. WT-ICA combined with SVM was proposed to remove OAs automatically.…”

“…Electroencephalogram (EEG) is a voltage test recording the electrical activity of the neurons in the brain. EEG is obtained in various frequencies including delta (1-4 Hz), theta (4-8 Hz), alpha , beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (greater than 30 Hz) [1]. EEG brain rhythms with different frequency ranges are shown in Table 1 [2].…”

“…It was composed of four components: EEG signal decomposition into two modes using VMD-I, rejection of low-frequency baseline components, processed EEG signal decomposition into three modes using VMD-II, rejection of mode containing OAs based on turning point count-based threshold criteria.The OAs were eliminated with minimal loss in the entire local rhythms and reconstructed EEG signals. Liu et al[25] have labeled a robust and effective muscle artifact removal technique with the help of fast multivariate empirical mode decomposition (FMEMD) and CCA in the case of few-channel EEG. The input EEG recordings were decomposed into various multivariate IMFs.…”

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

“…In addition, it also requires an expertise to select the bad channel. [67] to remove the muscle artifact. EMD may not be applicable to real-time system and it doesn't require reference channel.…”