Search results for "Décomposition modale empirique"
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Online Detection and Removal of Eye Blink Artifacts from Electroencephalogram
2020
The most prominent type of artifact contaminating electroencephalogram (EEG)signals are the eyeblink (EB) artifacts, which could potentially lead tomisinterpretation of the EEG signal. Online detection and removal of eyeblink artifacts from EEG signals are essential in applications such a Brain-Computer Interfaces (BCI), neurofeedback and epilepsy diagnosis. In this thesis, algorithms that combine unsupervised eyeblink artifact detection (eADA) with enhanced Empirical Mode Decomposition (FastEMD) and Canonical Correlation Analysis (CCA) are proposed,i.e. FastEMD-CCA2 and FastCCA, to automatically identify eyeblink artifacts andremove them in an online setting. FastEMD-CCA2 and FastCCA have …