RT Journal Article
SR Electronic
T1 Decoding the auditory brain with canonical component analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 217281
DO 10.1101/217281
A1 de Cheveigné, Alain
A1 Wong, Daniel
A1 Di Liberto, Giovanni
A1 Hjortkjaer, Jens
A1 Slaney, Malcolm
A1 Lalor, Edmund
YR 2017
UL http://biorxiv.org/content/early/2017/11/10/217281.abstract
AB 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.