Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech

Objective. Measurement of the cortical tracking of continuous speech from
electroencephalography (EEG) recordings using a forward model is an important tool in
auditory neuroscience. Usually the stimulus is represented by its temporal envelope. Recently,
the phonetic representation of speech was successfully introduced in English. We aim to
show that the EEG prediction from phoneme-related speech features is possible in Dutch. The
method requires a manual channel selection based on visual inspection or prior knowledge to
obtain a summary measure of cortical tracking. We evaluate a method to (1) remove nonstimulus-related activity from the EEG signals to be predicted, and (2) automatically select
the channels of interest. Approach. Eighteen participants listened to a Flemish story, while
their EEG was recorded. Subject-specific and grand-average temporal response functions
were determined between the EEG activity in different frequency bands and several stimulus
features: the envelope, spectrogram, phonemes, phonetic features or a combination. The
temporal response functions were used to predict EEG from the stimulus, and the predicted
was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus
features. A spatial filter was calculated based on the generalized eigenvalue decomposition
(GEVD), and the effect on EEG prediction accuracy was determined. Main results. A model
including both low- and high-level speech representations was able to better predict the brain
responses to the speech than a model only including low-level features. The inclusion of a
GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses
to each speech feature at both single-subject (270% improvement) and group-level (310%).
Significance. We showed that the inclusion of acoustical and phonetic speech information
and the addition of a data-driven spatial filter allow improved modelling of the relationship
between the speech and its brain responses and offer an automatic channel selection.