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Neuroimage. 2018 May 15;172:206-216. doi: 10.1016/j.neuroimage.2018.01.033. Epub 2018 Jan 31.

Decoding the auditory brain with canonical component analysis.

Author information

1
Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, France; UCL Ear Institute, United Kingdom. Electronic address: Alain.de.Cheveigne@ens.fr.
2
Laboratoire des Systèmes Perceptifs, UMR 8248, CNRS, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, France.
3
Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Allé 30, 2650, Hvidovre, Denmark.
4
Google AI for Perception, United States.
5
Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States; Department of Neuroscience, University of Rochester, Rochester, NY, United States; School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.

Abstract

The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation 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 continuous 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 the stimulus and the 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 observations of the relation between stimulus and response.

KEYWORDS:

CCA; Canonical correlation; EEG; ICA; LFP; MEG; Modulation filter; PCA; Reverse correlation; Speech; TRF

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