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.
Highlights
As a representatively noninvasive technique of reflecting electrical activities generated by the cerebral cortex, electroencephalography (EEG) is widely used for numerous practical applications in the biomedical engineering field
EEG recordings are important for the description of the irritant and ictal onset zones in the presurgical evaluation of refractory partial epilepsy [1]; motor imagery EEG signals provide an important basis for designing a way to communicate between the brain and computer [2]; by making use of sparse EEG compressive sensing, person identification is possible [3]; and EEG can be utilized with other physiological data of different types to make a study of brain functions [4]
To make a quantitative comparison, the semisimulated data were handled by all the methods in this paper to automatically eliminate EMG artifacts. ere were a total of 20 collected EEG recordings generated from 20 different subjects
Summary
As a representatively noninvasive technique of reflecting electrical activities generated by the cerebral cortex, electroencephalography (EEG) is widely used for numerous practical applications in the biomedical engineering field. It owns the benefits of low cost, easy usability, and high temporal resolution. With relatively low amplitudes, EEG is often polluted by many kinds of nonbrain artifacts mainly from the electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) interferences. Empirical mode decomposition (EMD), as one well-known decomposition method firstly suggested by Huang et al, is suitable to process many kinds of time variable and complex signals. A single-channel signal x(t) can be decomposed in the form of
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