Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia
Year: 2011
Type: conference-paper
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.
Authors: K. F. K. Wong, E. A. Mukamel, A. F. Salazar, E. T. Pierce, P. G. Harrell, J. L. Walsh, A. Sampson, E. N. Brown, P. L. Purdon
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Multimodal Functional Imaging of Auditory Perception Under General Anesthesia, A Neural Systems Approach to Monitoring and Drug-Delivery for General Anesthesia, A Systems Neuroscience Approach for the Study of General Anesthesia, Dynamic Inverse Solutions for Multimodal Imaging, Training in Neurostatistics and Neuroepidemiology, Dynamic Analyses of Learning in Behavioral Experiments
OpenAlex ID: W2045994787