Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia
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Brown_Robust time-varying.pdf
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Author(s)
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
Date Issued
August 2011
Journal
Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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.).
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Author's final manuscript
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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DOI of Published Version
https://doi.org/10.1109/IEMBS.2011.6091170
