Elsevier

NeuroImage

Volume 172, 15 May 2018, Pages 206-216
NeuroImage

Decoding the auditory brain with canonical component analysis

https://doi.org/10.1016/j.neuroimage.2018.01.033Get rights and content
Under a Creative Commons license
open access

Highlights

  • CCA is a powerful, easy to use method for multichannel data analysis.

  • CCA finds an optimal linear model to relate stimulus and brain response.

  • Multiple speech components map to distinct spectro-spatial signatures.

  • CCA yields large stimulus-response correlation values.

  • CCA supports good performance in a classification task.

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

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

Cited by (0)