  Various new brain-computer interface technologies or neuroscience
applications require decoding stimulus-following neural responses to natural
stimuli such as speech and video from, e.g., electroencephalography (EEG)
signals. In this context, generalized canonical correlation analysis (GCCA) is
often used as a group analysis technique, which allows the extraction of
correlated signal components from the neural activity of multiple subjects
attending to the same stimulus. GCCA can be used to improve the signal-to-noise
ratio of the stimulus-following neural responses relative to all other
irrelevant (non-)neural activity, or to quantify the correlated neural activity
across multiple subjects in a group-wise coherence metric. However, the
traditional GCCA technique is stimulus-unaware: no information about the
stimulus is used to estimate the correlated components from the neural data of
several subjects. Therefore, the GCCA technique might fail to extract relevant
correlated signal components in practical situations where the amount of
information is limited, for example, because of a limited amount of training
data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA)
framework that allows taking the stimulus into account to extract the
correlated components. We show that SI-GCCA outperforms GCCA in various
practical settings, for both auditory and visual stimuli. Moreover, we showcase
how SI-GCCA can be used to steer the estimation of the components towards the
stimulus. As such, SI-GCCA substantially improves upon GCCA for various
purposes, ranging from preprocessing to quantifying attention.
