Paper Info
Title
Automatic removal of high-amplitude artefacts from single-channel electroencephalograms
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.
Year
DOI
Venue
2006
10.1016/j.cmpb.2006.06.003
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
Singular spectrum analysis (SSA),Embedding,Principal component analysis,Electrooculogram (EOG),Electroencephalogram (EEG)
Computer vision,Embedding,Computer science,Communication channel,Artificial intelligence,Singular spectrum analysis,Statistics,Cluster analysis,Amplitude,Eigenvalues and eigenvectors,Principal component analysis,Electroencephalography
Journal
Volume
Issue
ISSN
83
2
0169-2607
Citations 
PageRank 
References 
16
1.03
3
Authors
5
Name
Order
Citations
PageRank
Ana R. Teixeira1343.77
Ana Maria Tomé216330.42
Elmar Wolfgang Lang326036.10
P. Gruber4515.80
A. Martins Da Silva5232.41