Multiway canonical correlation analysis of brain data

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

321 Downloads (Pure)


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
Pages (from-to)728-740
Publication statusPublished - 2018


  • CCA
  • EEG
  • Generalized CCA
  • Multiple CCA
  • Multivariate CCA
  • Multiway CCA


Dive into the research topics of 'Multiway canonical correlation analysis of brain data'. Together they form a unique fingerprint.

Cite this