Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis

Abstract

Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

“…The main characteristics of SSA are neither a parametric model nor stationary condition have to be assumed for a time series. Whilst the review of all applications of SSA is beyond the scope of this paper, we refer interested readers to [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] . For a whole and detailed information on the theory and applications of SSA, see [50] , [51] .…”

Evaluating ecological risks and tracking potential factors influencing heavy metals in sediments in an urban river

“…Canonical correlation analysis (CCA) and principal component analysis (PCA) are the most common multivariate statistical methods used in environmental studies, which provide techniques for classifying interrelationship of heavy metals and samples [14,15]. CCA could elaborate on the relationship between observed variables, and track the latent factors [16]. PCA is widely used to reduce data, extract a small number of latent factors, and discriminate the similarity and dissimilarity for analyzing relationships among the observed variables [17].…”

A new era in electroencephalographic monitoring? Subscalp devices for ultra–long‐term recordings

“…Many algorithms have been proposed to remove noise from EEG 30 ; however, especially for the modalities with only a few channels, this will be challenging although not entirely impossible. 31 Besides the implant, an external unit for power, data storage, and transmission is needed. Five of six solutions have opted for transmission of the data out of the implant (see below).…”

“…Sleep recordings are also improved because subscalp electrodes are less obtrusive than scalp electrodes in the recumbent (sleeping) position. Many algorithms have been proposed to remove noise from EEG 30 ; however, especially for the modalities with only a few channels, this will be challenging although not entirely impossible 31 …”