Artifact removal from EEG data with empirical mode decomposition

Vadim V. Grubov; Anastasiya E. Runnova; Tatyana Yu. Efremova; Alexander E. Hramov

doi:10.1117/12.2251371

20 March 2017 Artifact removal from EEG data with empirical mode decomposition

Vadim V. Grubov, Anastasiya E. Runnova, Tatyana Yu. Efremova, Alexander E. Hramov

Proceedings Volume 10063, Dynamics and Fluctuations in Biomedical Photonics XIV; 100631F (2017) https://doi.org/10.1117/12.2251371
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Citation Download Citation

Vadim V. Grubov, Anastasiya E. Runnova, Tatyana Yu. Efremova, and Alexander E. Hramov "Artifact removal from EEG data with empirical mode decomposition", Proc. SPIE 10063, Dynamics and Fluctuations in Biomedical Photonics XIV, 100631F (20 March 2017); https://doi.org/10.1117/12.2251371

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