Multiway canonical correlation analysis of brain data
Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is no...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 186; pp. 728 - 740 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.02.2019
Elsevier Limited Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2018.11.026 |
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| Abstract | Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.
•MCCA combines multiple data sets into a common representation.•MCCA can be used to summarize data across subjects.•MCCA can be used to denoise data, or reduce dimensionality, based on consistency across subjects. |
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| AbstractList | Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method. Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method. •MCCA combines multiple data sets into a common representation.•MCCA can be used to summarize data across subjects.•MCCA can be used to denoise data, or reduce dimensionality, based on consistency across subjects. Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method. |
| Author | Fuglsang, Søren Parra, Lucas C. Di Liberto, Giovanni M. de Cheveigné, Alain Arzounian, Dorothée Hjortkjær, Jens Wong, Daniel D.E. |
| Author_xml | – sequence: 1 givenname: Alain surname: de Cheveigné fullname: de Cheveigné, Alain email: Alain.de.Cheveigne@ens.fr organization: Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France – sequence: 2 givenname: Giovanni M. surname: Di Liberto fullname: Di Liberto, Giovanni M. organization: Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France – sequence: 3 givenname: Dorothée surname: Arzounian fullname: Arzounian, Dorothée organization: Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France – sequence: 4 givenname: Daniel D.E. surname: Wong fullname: Wong, Daniel D.E. organization: Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France – sequence: 5 givenname: Jens surname: Hjortkjær fullname: Hjortkjær, Jens organization: Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Denmark – sequence: 6 givenname: Søren surname: Fuglsang fullname: Fuglsang, Søren organization: Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Denmark – sequence: 7 givenname: Lucas C. surname: Parra fullname: Parra, Lucas C. organization: City College New York, USA |
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| Keywords | CCA Multiple CCA Multivariate CCA Multiway CCA EEG Generalized CCA |
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