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DOI: https://doi.org/10.31071/kit2019.15.01


Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 14, Issue 15, 2019, Pages 13-34


Author(s) I. V. Redka


Institution(s)

Kharkiv Medical Academy of Postgraduate Education, Ukraine

Kharkiv National University named after V. N. Karazin, Ukraine


Article title Modern approaches to detecting and removing artifacts from EEG signals. Overview


Abstract (resume)

Introduction. The detection of artifacts and their subsequent removal from EEG data is an important problem in neurophysiology, since the characteristics of artifact fragments overlap with those of true brain activity.

The aim is a review of existing mathematical approaches to the recognition and removal of artifacts from EEG signals.

Results. There are the methods based on statistical artifact rejection, adaptive filtration, regression, blind source separation, empirical mode decomposition, wavelet transform and their combination. Each of them has its advantages and disadvantages. Nevertheless, there is no universal algorithm for all types of artifacts. Choosing an artifact removal algorithm is mainly based on a presence of a reference channel, a method for detecting artifacts (automated or expert), and an artifact detection/removal mode (оn-line or off-line). It is quite difficult to compare different methods of artifact rejection due to the use of different metrics based on their ability to remove artifacts and the degree of distortion of the output signal.

Conclusion. Independent Components Analysis is the most commonly used in neurophysiological research. The priority is the development of hybrid methods for the removal of physiological artifacts. A three-step verification of the effectiveness of the new algorithm for identifying/removing artifacts is advisable.


Keywords EEG, Artifacts, Regression, Adaptive filtering, Blind separation of sources, Empirical mode decomposition, Wavelet-transform, Threshold values


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