Journal
NEUROIMAGE
Volume 186, Issue -, Pages 728-740Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.11.026
Keywords
EEG; CCA; Generalized CCA; Multiple CCA; Multiway CCA; Multivariate CCA
Funding
- EU [644732]
- National Science Foundation [DRL-1660548]
- [ANR-10-LABX-0087 IEC]
- [ANR-10-IDEX-0001-02 PSL*]
- [ANR-17-EURE-0017]
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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.
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