Abstract
Electromyography (EMG) is the superposition of motor unit action potential (MUAP) in many muscle fibers in time and space. In real measurement, EMG signals will contaminate Electromyography signals, therefore they bring great difficulties to the qualified analysis and interpretation of EEG signals, and it is a momentous step to remove EMG artifacts from EEG signals. In the recent years, new methods were developed for EEG artifacts removal such as Multivariate Empirical Mode Decomposition and Singular Spectrum Analysis. In particular, some researchers combined the two methods and used their respective advantages to remove artifacts more thoroughly without affecting the EEG signal, such as the combination of Independent Component Analysis and Wavelet Method. In this paper, new methods for muscular artifacts removal from EEG above are discussed. Moreover, traditional methods including signal transform, filtering methods and Blind source separation (BSS) are also reviewed.
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