Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia
Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of v...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2014
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| Online Access: | http://hdl.handle.net/1721.1/86326 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 |
| _version_ | 1826190980279173120 |
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| author | Brown, Emery N. Purdon, Patrick Lee Wong, Kin Foon Kevin Mukamel, Eran A. Salazar, Andres Felipe Pierce, Eric T. Harrell, P. Grace Walsh, John L. Sampson, Aaron |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Brown, Emery N. Purdon, Patrick Lee Wong, Kin Foon Kevin Mukamel, Eran A. Salazar, Andres Felipe Pierce, Eric T. Harrell, P. Grace Walsh, John L. Sampson, Aaron |
| author_sort | Brown, Emery N. |
| collection | MIT |
| description | Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time. |
| first_indexed | 2024-09-23T08:48:14Z |
| format | Article |
| id | mit-1721.1/86326 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:48:14Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/863262022-09-23T14:39:14Z Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia Brown, Emery N. Purdon, Patrick Lee Wong, Kin Foon Kevin Mukamel, Eran A. Salazar, Andres Felipe Pierce, Eric T. Harrell, P. Grace Walsh, John L. Sampson, Aaron Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Brown, Emery N. Purdon, Patrick Lee Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time. National Institutes of Health (U.S.) (Grant DP2-OD006454) National Institutes of Health (U.S.) (Grant K25-NS057580) National Institutes of Health (U.S.) (Grant DP1-OD003646) National Institutes of Health (U.S.) (Grant R01-EB006385) National Institutes of Health (U.S.) (Grant R01-MH071847) 2014-05-01T15:46:10Z 2014-05-01T15:46:10Z 2011-08 2011-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4577-1589-1 978-1-4244-4121-1 978-1-4244-4122-8 http://hdl.handle.net/1721.1/86326 Wong, K. F. K., E. A. Mukamel, A. F. Salazar, E. T. Pierce, P. G. Harrell, J. L. Walsh, A. Sampson, E. N. Brown, and P. L. Purdon. “Robust Time-Varying Multivariate Coherence Estimation: Application to Electroencephalogram Recordings During General Anesthesia.” 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (n.d.). https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 en_US http://dx.doi.org/10.1109/IEMBS.2011.6091170 Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) PMC |
| spellingShingle | Brown, Emery N. Purdon, Patrick Lee Wong, Kin Foon Kevin Mukamel, Eran A. Salazar, Andres Felipe Pierce, Eric T. Harrell, P. Grace Walsh, John L. Sampson, Aaron Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title | Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title_full | Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title_fullStr | Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title_full_unstemmed | Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title_short | Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia |
| title_sort | robust time varying multivariate coherence estimation application to electroencephalogram recordings during general anesthesia |
| url | http://hdl.handle.net/1721.1/86326 https://orcid.org/0000-0001-5651-5060 https://orcid.org/0000-0003-2668-7819 |
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