Delay Differential Analysis of Electroencephalographic Data
Identifieur interne : 001B13 ( Pmc/Curation ); précédent : 001B12; suivant : 001B14Delay Differential Analysis of Electroencephalographic Data
Auteurs : Claudia Lainscsek ; Manuel E. Hernandez ; Howard Poizner ; Terrence J. SejnowskiSource :
- Neural computation [ 0899-7667 ] ; 2014.
Abstract
We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.
Url:
DOI: 10.1162/NECO_a_00656
PubMed: 25149701
PubMed Central: 4372301
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<author><name sortKey="Hernandez, Manuel E" sort="Hernandez, Manuel E" uniqKey="Hernandez M" first="Manuel E." last="Hernandez">Manuel E. Hernandez</name>
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<author><name sortKey="Poizner, Howard" sort="Poizner, Howard" uniqKey="Poizner H" first="Howard" last="Poizner">Howard Poizner</name>
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<author><name sortKey="Sejnowski, Terrence J" sort="Sejnowski, Terrence J" uniqKey="Sejnowski T" first="Terrence J." last="Sejnowski">Terrence J. Sejnowski</name>
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<front><div type="abstract" xml:lang="en"><p id="P1">We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.</p>
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<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9426182</journal-id>
<journal-id journal-id-type="pubmed-jr-id">20143</journal-id>
<journal-id journal-id-type="nlm-ta">Neural Comput</journal-id>
<journal-id journal-id-type="iso-abbrev">Neural Comput</journal-id>
<journal-title-group><journal-title>Neural computation</journal-title>
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<issn pub-type="ppub">0899-7667</issn>
<issn pub-type="epub">1530-888X</issn>
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<article-id pub-id-type="manuscript">HHMIMS672963</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>Delay Differential Analysis of Electroencephalographic Data</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Lainscsek</surname>
<given-names>Claudia</given-names>
</name>
<email>claudia@salk.edu</email>
<aff id="A1">Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, U.S.A.</aff>
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<contrib contrib-type="author"><name><surname>Hernandez</surname>
<given-names>Manuel E.</given-names>
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<email>mhernand@illinois.edu</email>
<aff id="A2">Institute for Neural Computation, University of California San Diego, La Jolla, CA 92903, U.S.A.</aff>
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<contrib contrib-type="author"><name><surname>Poizner</surname>
<given-names>Howard</given-names>
</name>
<email>hpoizner@ucsd.edu</email>
<aff id="A3">Institute for Neural Computation and Graduate Program in Neurosciences, University of California San Diego, La Jolla, CA 92903, U.S.A.</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Sejnowski</surname>
<given-names>Terrence J.</given-names>
</name>
<email>terry@salk.edu</email>
<aff id="A4">Howard Hughes Medical Institute, Computational Neurobiology Laboratory, La Jolla, CA 92037, U.S.A., and Institute for Neural Computation, University of California San Diego, La Jolla, CA 92903, U.S.A.</aff>
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<pub-date pub-type="nihms-submitted"><day>19</day>
<month>3</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>22</day>
<month>8</month>
<year>2014</year>
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<pub-date pub-type="ppub"><month>3</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>24</day>
<month>3</month>
<year>2015</year>
</pub-date>
<volume>27</volume>
<issue>3</issue>
<fpage>615</fpage>
<lpage>627</lpage>
<pmc-comment>elocation-id from pubmed: 10.1162/NECO_a_00656</pmc-comment>
<permissions><copyright-statement>© 2015 Massachusetts Institute of Technology</copyright-statement>
<copyright-year>2015</copyright-year>
</permissions>
<abstract><p id="P1">We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.</p>
</abstract>
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</front>
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