Decoding the auditory brain with canonical component analysis

Alain de Cheveigné*, Daniel D E Wong, Giovanni M Di Liberto, Jens Hjortkjær, Malcolm Slaney, Edmund Lalor

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
JournalNeuroImage
Volume172
Pages (from-to)206-216
ISSN1053-8119
DOIs
Publication statusPublished - 2018

Keywords

  • CCA
  • Canonical correlation
  • EEG
  • ICA
  • LFP
  • MEG
  • Modulation filter
  • PCA
  • Reverse correlation
  • Speech
  • TRF
  • Journal Article

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