Lesenfants, D. | bioRxiv Science

bioRxiv2019

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

ObjectiveMeasurement of the cortical tracking of continuous natural speech from electroencephalography (EEG) recordings using a forward model is becoming an important tool in auditory neuroscience. However, it requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. In this study, we present a method to on the one hand remove non-stimulus-related activity from the EEG signals to be predicted, and on the other hand automatically select the channels of interest. We also aim to show that the EEG prediction from phonology-related speech features is possible in Dutch. ApproachEighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal responses 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 then 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 resultsA 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 %). SignificanceWe 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 response and offer an automatic channel selection. HighlightsO_LIAutomatic channel selection for evaluating the cortical tracking of continuous natural speech C_LIO_LIData-driven spatial filtering for removing non-stimulus-related activity from the EEG signals C_LIO_LIImproved prediction of brain responses to speech by combining acoustical and phonetic speech information in Dutch C_LI DisclosureThe authors report no disclosures relevant to the manuscript.

neuroscience↗

bioRxiv2018

Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations

ObjectiveTo objectively measure speech intelligibility of individual subjects from the EEG, based on cortical tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination. To compare each models prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT.\n\nMethodsNineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of phonetic Features and Spectrogram (FS). The same model was used for all subjects. The model predictions were then compared to the actual EEG of each subject for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT.\n\nResultsThe model based on the FS speech representation and the theta EEG band yielded the best SRT predictions, with a difference between the behavioral and objective SRT below 1 decibel for 53% and below 2 decibels for 89% of the subjects.\n\nConclusionA model including low- and higher-level speech features allows to predict the speech reception threshold from the EEG of people listening to natural speech. It has potential applications in diagnostics of the auditory system.\n\nSearch Termscortical speech tracking, objective measure, speech intelligibility, auditory processing, speech representations.\n\nHighlightsO_LIObjective EEG-based measure of speech intelligibility\nC_LIO_LIImproved prediction of speech intelligibility by combining speech representations\nC_LIO_LICortical tracking of speech in the delta EEG band monotonically increased with SNRs\nC_LIO_LICortical responses in the theta EEG band best predicted the speech reception threshold\nC_LI\n\nDisclosureThe authors report no disclosures relevant to the manuscript.

neuroscience↗