NEW ALGORITHM FOR EEG AND EMG SEPARATION
Keywords:
BSS, variance of variances, gradient, muscular artifacts, EMG, EEGAbstract
The paper presents newly proposed algorithm for the blind separation of EEG and EMG sources measured by high density electrode arrays. The algorithm is based on the maximization of the variance of variances of filtered principal components. Utilized high pass filter was optimized in order to extract the information which is used by the gradient algorithm to separate EEG and EMG components. The performance of the algorithm was evaluated by its use for the muscular artifacts removal. Present muscular artifacts were extracted from the estimated components with the use of the previously used classifier. It is compared with other similar approaches and it is shown that the suggested algorithm achieves higher quality of the processed EEG signal especially in the case of strong muscular artifacts and is therefore useful for the preprocessing of the EEG records contaminated with the muscle activity.
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Copyright (c) 2017 Jan Šebek, Radoslav Bortel
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