Please use this identifier to cite or link to this item
|title: ||Automatic removal of high-amplitude artefacts from single-channnel electroencephalograms|
|authors: ||Teixeira, A. R.|
Tomé, A. M.
Lang, E. W.
Silva, A. Martins da
|keywords: ||Singular spectrum analysis (SSA)|
Principal component analysis
|issue date: ||Jul-2006|
|abstract: ||In this work, we present a method to extract high-amplitude artefacts from single channel
electroencephalogram (EEG) signals. The method is called local singular spectrum analysis
(local SSA). It is based on a principal component analysis (PCA) applied to clusters of the
multidimensional signals obtained after embedding the signals in their time-delayed coordinates.
The decomposition of the multidimensional signals in each cluster is achieved by
relating the largest eigenvalues with the large amplitude artefact component of the embedded
signal. Then by reverting the clustering and embedding processes, the high-amplitude
artefact can be extracted. Subtracting it from the original signal a corrected EEG signal results.
The algorithm is applied to segments of real EEG recordings containing paroxysmal
epileptiform activity contaminated by large EOG artefacts. We will show that the method
can be applied also in parallel to correct all channels that present high-amplitude artefacts
like ocular movement interferences or high-amplitude low frequency baseline drifts. The
extracted artefacts as well as the corrected EEG will be presented.|
|publisher version/DOI: ||http://dx.doi.org/10.1016/j.cmpb.2006.06.003|
|source: ||Computer Methods and Programs in Biomedicine|
|appears in collections||DETI - Artigos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.