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Multiway canonical correlation analysis of brain data

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  • Alain de Cheveigné
  • Giovanni M Di Liberto
  • Dorothée Arzounian
  • Daniel D E Wong
  • Jens Hjortkjær
  • Søren Fuglsang
  • Lucas C Parra
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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.

Original languageEnglish
JournalNeuroImage
Volume186
Pages (from-to)728-740
Number of pages13
ISSN1053-8119
DOIs
Publication statusPublished - 1 Feb 2019

    Research areas

  • CCA, EEG, Generalized CCA, Multiple CCA, Multivariate CCA, Multiway CCA, Models, Theoretical, Data Interpretation, Statistical, Electroencephalography/methods, Humans, Brain/physiology, Magnetoencephalography/methods, Adult

ID: 55749879