Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses

TL;DR

This paper introduces a stimulus-informed GCCA method that improves the extraction of correlated neural responses to stimuli, especially with limited data or small subject groups.

Contribution

A novel stimulus-informed GCCA algorithm based on MAXVAR-GCCA that incorporates stimulus information to enhance inter-subject correlation analysis.

Findings

Outperforms traditional GCCA in low-data scenarios

Better correlation extraction with smaller subject groups

Effective in analyzing EEG responses to speech stimuli

Abstract

In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural activity of different subjects attending to the same stimulus. This allows quantifying the so-called inter-subject correlation or boosting the signal-to-noise ratio of the stimulus-following brain responses with respect to other (non-)neural activity. GCCA is, however, stimulus-unaware: it does not take the stimulus information into account and does therefore not cope well with lower amounts of data or smaller groups of subjects. We propose a novel stimulus-informed GCCA algorithm based on the MAXVAR-GCCA framework. We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects.