The geometry of time-varying cross-correlation random fields

F. Carbonell; K. J. Worsley; N. J. Trujillo-Barreto; M. Vega-Hernandez

doi:10.1016/j.csda.2009.02.019

The geometry of time-varying cross-correlation random fields

F. Carbonell, K. J. Worsley, N. J. Trujillo-Barreto, M. Vega-Hernandez

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Abstract

The goal of this paper is to assess the P-value of local maxima of time-varying cross-correlation random fields. The motivation for this comes from an electroencephalography (EEG) experiment, where one seeks connectivity between all pairs of voxels inside the brain at each time point of the recording window. In this way, we extend the results of [Cao, J., Worsley, K.J., 1999b. The geometry of correlation fields with an application to functional connectivity of the brain. The Annals of Applied Probability 9 (4), 1021-1057] by searching for high correlations not only over all pairs of voxels, but over all time points as well. We apply our results to an EEG data set of a face recognition paradigm. Our analysis determines those time instants for which there are significantly correlated regions involved in face recognition. © 2009 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	3291-3304
Number of pages	13
Journal	Computational Statistics and Data Analysis
Volume	53
Issue number	9
DOIs	https://doi.org/10.1016/j.csda.2009.02.019
Publication status	Published - 1 Jul 2009

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10.1016/j.csda.2009.02.019

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title = "The geometry of time-varying cross-correlation random fields",

abstract = "The goal of this paper is to assess the P-value of local maxima of time-varying cross-correlation random fields. The motivation for this comes from an electroencephalography (EEG) experiment, where one seeks connectivity between all pairs of voxels inside the brain at each time point of the recording window. In this way, we extend the results of [Cao, J., Worsley, K.J., 1999b. The geometry of correlation fields with an application to functional connectivity of the brain. The Annals of Applied Probability 9 (4), 1021-1057] by searching for high correlations not only over all pairs of voxels, but over all time points as well. We apply our results to an EEG data set of a face recognition paradigm. Our analysis determines those time instants for which there are significantly correlated regions involved in face recognition. {\textcopyright} 2009 Elsevier B.V. All rights reserved.",

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AU - Carbonell, F.

AU - Worsley, K. J.

AU - Trujillo-Barreto, N. J.

AU - Vega-Hernandez, M.

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AB - The goal of this paper is to assess the P-value of local maxima of time-varying cross-correlation random fields. The motivation for this comes from an electroencephalography (EEG) experiment, where one seeks connectivity between all pairs of voxels inside the brain at each time point of the recording window. In this way, we extend the results of [Cao, J., Worsley, K.J., 1999b. The geometry of correlation fields with an application to functional connectivity of the brain. The Annals of Applied Probability 9 (4), 1021-1057] by searching for high correlations not only over all pairs of voxels, but over all time points as well. We apply our results to an EEG data set of a face recognition paradigm. Our analysis determines those time instants for which there are significantly correlated regions involved in face recognition. © 2009 Elsevier B.V. All rights reserved.

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DO - 10.1016/j.csda.2009.02.019

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JO - Computational Statistics and Data Analysis

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The geometry of time-varying cross-correlation random fields

Abstract

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