Multiway Canonical Correlation Analysis of Brain Signals
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 applic...
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| Published in | bioRxiv |
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| Main Authors | , , , , , , |
| Format | Paper |
| Language | English |
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
12.06.2018
Cold Spring Harbor Laboratory |
| Edition | 1.1 |
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| ISSN | 2692-8205 2692-8205 |
| DOI | 10.1101/344960 |
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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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| AbstractList | 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. |
| Author | De Cheveigne, Alain Arzounian, Dorothee Wong, Daniel Di Liberto, Giovanni M Hjortkjaer, Jens Soren Asp Fuglsang Parra, Lucas C |
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| Copyright | 2018. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ ( the License ). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018, Posted by Cold Spring Harbor Laboratory |
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| DOI | 10.1101/344960 |
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| Keywords | CCA CSP DSS EEG ICA generalized CCA MEG multiway CCA ECoG LFP multiple CCA SNS multivariate CCA mcca gcca |
| Language | English |
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| SubjectTerms | Correlation analysis EEG Magnetoencephalography Neuroscience |
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| Title | Multiway Canonical Correlation Analysis of Brain Signals |
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