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Multiway Canonical Correlation Analysis of Brain Signals

View ORCID ProfileAlain de Cheveigné, Giovanni M. Di Liberto, View ORCID ProfileDorothée Arzounian, Daniel D.E. Wong, View ORCID ProfileJens Hjortkjær, View ORCID ProfileSøren Fuglsang, Lucas C. Parra
doi: https://doi.org/10.1101/344960
Alain de Cheveigné
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France
2Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
3UCL Ear Institute, London, United Kingdom
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Giovanni M. Di Liberto
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France
2Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
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Dorothée Arzounian
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France
2Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
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Daniel D.E. Wong
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France
2Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
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Jens Hjortkjær
4Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark
5Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre
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Søren Fuglsang
4Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark
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Lucas C. Parra
6City College New York, USA
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Abstract

Brain signals recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratio due to the presence of multiple competing sources and artifacts. A common remedy is to average 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 the same 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 12, 2018.
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Multiway Canonical Correlation Analysis of Brain Signals
Alain de Cheveigné, Giovanni M. Di Liberto, Dorothée Arzounian, Daniel D.E. Wong, Jens Hjortkjær, Søren Fuglsang, Lucas C. Parra
bioRxiv 344960; doi: https://doi.org/10.1101/344960
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Multiway Canonical Correlation Analysis of Brain Signals
Alain de Cheveigné, Giovanni M. Di Liberto, Dorothée Arzounian, Daniel D.E. Wong, Jens Hjortkjær, Søren Fuglsang, Lucas C. Parra
bioRxiv 344960; doi: https://doi.org/10.1101/344960

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