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Automatic Removal of Sparse Artifacts in Electroencephalogram
Zima, Miroslav ; Tichavský, Petr ; Krajča, V.
This report presents an algorithm for removing artifacts from EEG signal, which is based on the method of independent component analysis utilizing the signal nonstationarity or sparsity of the artifacts. The algorithm is computationally very fast, enables online processing of long data records with excellent separation accuracy. The algorithm also incorporates using wavelet denoising of the artifact components, recently proposed by Castellanos and Makarov, which reduces distortion of the cleaned data.
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Artifact removal from EEG recordings III
Zima, Miroslav ; Tichavský, Petr ; Krajča, V.
Electroencephalogram (EEG) recordings are often corrupted by presence of unwanted artifact signals. This work is focused on removal of artifact that have a relatively short duration and a large amplitude - such as eye blinks, and patient movement artifacts. It presents a method of removal of these artifacts using methods of independent component analysis in short windows. The method is tested on neonatal (8 channel) EEG recordings. The recordings may have an arbitrary length.
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