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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Vydané v:	Journal of neuroscience methods Ročník 196; číslo 1; s. 190 - 200
Hlavní autori:	Lelic, Dina, Gratkowski, Maciej, Hennings, Kristian, Drewes, Asbjørn Mohr
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 15.03.2011
Predmet:	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
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Abstract	▶ 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.
AbstractList	▶ 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. 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. Methods: 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. Results: 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. Conclusion: 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. 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.INTRODUCTIONMultichannel 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.METHODSFour 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.RESULTSThe 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.CONCLUSIONThe 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. 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.
Author	Hennings, Kristian Drewes, Asbjørn Mohr Lelic, Dina Gratkowski, Maciej
Author_xml	– sequence: 1 givenname: Dina surname: Lelic fullname: Lelic, Dina organization: Mech-Sense, Department of Gastroenterology, Aalborg Hospital, Aarhus University, Denmark – sequence: 2 givenname: Maciej surname: Gratkowski fullname: Gratkowski, Maciej organization: Institute of Biomedical Engineering and Informatics, Computer Science and Automation Faculty, Ilmenau University of Technology, Ilmenau, Germany – sequence: 3 givenname: Kristian surname: Hennings fullname: Hennings, Kristian organization: Mech-Sense, Department of Gastroenterology, Aalborg Hospital, Aarhus University, Denmark – sequence: 4 givenname: Asbjørn Mohr surname: Drewes fullname: Drewes, Asbjørn Mohr email: amd@mech-sense.com organization: Mech-Sense, Department of Gastroenterology, Aalborg Hospital, Aarhus University, Denmark
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CitedBy_id	crossref_primary_10_1111_bcp_13177 crossref_primary_10_1016_j_neuroimage_2011_12_002 crossref_primary_10_1016_j_neuropharm_2013_10_020 crossref_primary_10_1111_ejn_13443 crossref_primary_10_1007_s00422_016_0707_5 crossref_primary_10_1097_WNP_0b013e3182570fd3 crossref_primary_10_1016_j_neuroscience_2013_01_049 crossref_primary_10_1124_pr_111_005447 crossref_primary_10_3390_e20060419 crossref_primary_10_1159_000375310 crossref_primary_10_1111_nmo_12222 crossref_primary_10_1002_j_1532_2149_2013_00442_x crossref_primary_10_1007_s00221_011_2913_7
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publication-title: J Clin Neurophysiol doi: 10.1097/WNP.0b013e3181aed1a1
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Snippet	▶ 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... Multichannel matching pursuit (MMP) is a relatively new method that can be applied to electroencephalogram (EEG) signals in combination with inverse modelling....
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SubjectTerms	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
Title	Multichannel matching pursuit validation and clustering—A simulation and empirical study
URI	https://dx.doi.org/10.1016/j.jneumeth.2010.12.021 https://www.ncbi.nlm.nih.gov/pubmed/21187116 https://www.proquest.com/docview/852912041 https://www.proquest.com/docview/907157630
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