Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

Abstract

BackgroundEnsemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.ExperimentThe experimental data was obtained from the Center for Machine Learning and Intelligent Systems, University of California Irvine (UCI). The data was donated by the Nueva Granada Military University and the Technopark node Manizales in Colombia. The databases of 11 male subjects from the healthy group were taken into the study. The subjects undergo three exercise programs, leg extension from a sitting position (sitting), flexion of the leg up (standing), and gait (walking), while four electrodes were placed on biceps femoris (BF), vastus medialis (VM), rectus femoris (RF), and semitendinosus (ST).MethodsBased on the experimental data, a comparative study is provided by assessing the Empirical Mode Decomposition (EMD)-based approaches, EEMD, Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). The outcomes from these approaches are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing.ResultsBoth MEMD and NA-MEMD methods (except EEMD) can guarantee equal numbers of IMFs. For mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD.ConclusionsThis study proposes the NA-MEMD approach for multichannel EMG signal processing. This finding implies that NA-MEMD is effective for simultaneously analysing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing the functional activities of daily living.

Causal decomposition in the mutual causation system

“…First, the causal decomposition represents a form of statistical causality and does not imply the true causality, which requires the inclusion of all variables to conclude the existence of causal relationship 3 . Second, the causal decomposition is limited to the pairwise measurement in the current form, but we do not exclude the possibility of the extension of the current method to multivariate systems (e.g., functional brain networks) with the employment of multivariate EMD 34 , 35 in the future. In that case, we have to define and work with the absolute causal strength matrix.…”

“…Therefore, the development of causal decomposition is not to complement existing methods, but to explore the use of covariation principle of cause and effect for assessing causality. With the potential of the extension of ensemble EMD to multivariate EMD 34 , 35 , we anticipate that this causal decomposition approach will assist with revealing causal interactions in complex networks not accounted for by current methods.…”

Removal of Artifacts from EEG Signals: A Review

“…Then the original signal can he reconstructed by:normalx[n]=∑k=1mbk(n)+r(n), where r(n) represent the final residue signal [74]. Once the IMF determined, the artifact components of EEG data can be reflected, and then selected and removed.…”

Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition

“…In order to overcome these disadvantages a new approach called noise-assisted multivariate empirical mode decomposition (NA-MEMD) has recently been developed [24]. As an important step in data-adaptive analysis, the applications of NA-MEMD have so far been utilized resulting in positive outcomes, which are based on time-frequency axes with Doppler radar signals computer simulations and motor image EEG data from the BCI competition IV data set in time-frequency analysis of neuronal populations with instantaneous resolution phase synchronization using EEG-based prediction of epileptic seizures, in lung-heart sound discrimination, multichannel EMG signals, and rejecting the unwanted noise contained within the VLF-EM (very low-frequency electromagnetic method) data, which produced NA-MEMD [25][26][27][28][29][30][31]. Additionally, as an improved noise assisted method for multivariate signals decomposition, partial noise assisted multivariate EMD is proposed by Huang et al [32].…”