Multichannel matching pursuit validation and clustering—A simulation and empirical study

▶ MMP accuracy decreases as the number of brain sources increases. ▶ MMP is robust to noise. ▶ We propose a modified K-means clustering to automate MMP analysis. Multichannel matching pursuit (MMP) is a relatively new method that can be applied to electroencephalogram (EEG) signals in combination wi...

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Bibliographic Details
Published in:Journal of neuroscience methods Vol. 196; no. 1; pp. 190 - 200
Main Authors: Lelic, Dina, Gratkowski, Maciej, Hennings, Kristian, Drewes, Asbjørn Mohr
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 15.03.2011
Subjects:
Adult
Algorithms
Analysis of Variance
Brain - physiology
Brain Mapping
Computer Simulation
EEG
Electroencephalography
Evoked Potentials, Auditory, Brain Stem - physiology
Evoked Potentials, Somatosensory - physiology
Female
Humans
K-mean clustering
Magnetic Resonance Imaging
Male
Median Nerve - physiology
Models, Neurological
Multichannel matching pursuit
Physical Stimulation
Reaction Time
Reproducibility of Results
Signal Processing, Computer-Assisted
Source analysis
Young Adult
K-mean clustering
Source analysis
Multichannel matching pursuit
EEG
ISSN:0165-0270, 1872-678X, 1872-678X
Online Access:Get full text
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Summary:▶ MMP accuracy decreases as the number of brain sources increases. ▶ MMP is robust to noise. ▶ We propose a modified K-means clustering to automate MMP analysis. Multichannel matching pursuit (MMP) is a relatively new method that can be applied to electroencephalogram (EEG) signals in combination with inverse modelling. However, limitations of MMP have not been adequately tested. The aims of this study were to investigate how the accuracy of MMP algorithm is altered due to increased number of brain sources and increased noise level, and to implement and test a modified K-means clustering algorithm in order to group similar MMP atoms in time–frequency and space between subjects together. Four groups of 20 EEG signals were simulated. The groups consisted of simulations with 5, 10, 15, and 20 brain sources. The accuracy of MMP algorithm was first tested on increasing number of sources. Then, different levels of noise were added to the simulations and accuracy of the algorithm was tested on increasing noise level. K-means clustering algorithm was tested on 4 datasets (5, 10, 15, and 20 sources) of 10 similar phantom subjects. Finally, the clustering algorithm was tested on empirical somatosensory evoked potential and brainstem evoked potential data. The MMP accuracy decreased as the number of sources increased and MMP accuracy was robust to noise. Furthermore, we found that when applying the clustering method to a subject group's MMP data, the clustering method grouped the similar atoms between subjects correctly. The MMP and clustering method proved to be an efficient way to group similar brain activity and thus study differences in brain activation sequence to sensory stimulation between groups of subjects.
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ISSN:0165-0270
1872-678X
1872-678X
DOI:10.1016/j.jneumeth.2010.12.021