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Decoding the auditory brain with canonical component analysis

View ORCID ProfileAlain de Cheveigné, Daniel Wong, Giovanni Di Liberto, View ORCID ProfileJens Hjortkjaer, View ORCID ProfileMalcolm Slaney, Edmund Lalor
doi: https://doi.org/10.1101/217281
Alain de Cheveigné
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS.
2Département d’Etudes Cognitives, Ecole Normale Supérieure.
3UCL Ear Institute.
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Daniel Wong
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS.
2Département d’Etudes Cognitives, Ecole Normale Supérieure.
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Giovanni Di Liberto
1Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS.
2Département d’Etudes Cognitives, Ecole Normale Supérieure.
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Jens Hjortkjaer
4Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800 Kgs. Lyngby, Denmark.
5Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Allé 30, 2650 Hvidovre, Denmark.
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Malcolm Slaney
6Google Machine Hearing Research.
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Edmund Lalor
7Department of Biomedical Engineering & Department of Neuroscience, University of Rochester, Rochester, NY.
8School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin.
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Abstract

The relation between a stimulus and the brain response that it evokes can shed light on perceptual processes within the brain. It can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated “decoding” strategies are needed to address ongoing stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both stimulus and response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves our observation of the relation between stimulus and response.

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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 November 10, 2017.
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Decoding the auditory brain with canonical component analysis
Alain de Cheveigné, Daniel Wong, Giovanni Di Liberto, Jens Hjortkjaer, Malcolm Slaney, Edmund Lalor
bioRxiv 217281; doi: https://doi.org/10.1101/217281
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Decoding the auditory brain with canonical component analysis
Alain de Cheveigné, Daniel Wong, Giovanni Di Liberto, Jens Hjortkjaer, Malcolm Slaney, Edmund Lalor
bioRxiv 217281; doi: https://doi.org/10.1101/217281

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