Multiway canonical correlation analysis of brain data

• Published in: NeuroImage (Orlando, Fla.) Vol. 186; pp. 728 - 740
• Main Authors: de Cheveigné, Alain, Di Liberto, Giovanni M., Arzounian, Dorothée, Wong, Daniel D.E., Hjortkjær, Jens, Fuglsang, Søren, Parra, Lucas C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2019; Elsevier Limited; Elsevier
• Subjects: Brain; CCA; Correlation analysis; Data analysis; Data processing; EEG; Electroencephalography; Generalized CCA; Life Sciences; Magnetoencephalography; Multiple CCA; Multivariate CCA; Multiway CCA; Neurons and Cognition; Noise; Sensors; CCA; Multiple CCA; Multivariate CCA; Multiway CCA; EEG; Generalized CCA
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.11.026

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.