A morphological way to remove baseline and spike separation in EEG

In this work, we have considered the problem of inconsistent sequence order and varying quality of decompositions by independent component analysis (ICA) and presented an alternate way to separate source signals from the electroencephalogram (EEG) using morphological component analysis (MCA) method...

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Published in2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2019; pp. 668 - 671
Main Authors Singh, Balbir, Mahapatra, Arindam Gajendra
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2019
Subjects
Algorithms
Correlation
Dictionaries
Discrete cosine transforms
Electrodes
Electroencephalography
Electrooculography
Records
Signal Processing, Computer-Assisted
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Summary:In this work, we have considered the problem of inconsistent sequence order and varying quality of decompositions by independent component analysis (ICA) and presented an alternate way to separate source signals from the electroencephalogram (EEG) using morphological component analysis (MCA) method based on explicit dictionary of independent redundant bases. Using correlation, qualitatively we have compared ICA with MCA's capability to segregate baseline and spike in different EEG data.
ISSN:1557-170X
1558-4615
DOI:10.1109/EMBC.2019.8856828