Marlies Gillis

This study examines neural dynamics of phoneme processing in children with and without dyslexia using EEG, revealing altered encoding strategies and processing inefficiencies in dyslexia.

The paper shows that individual differences in neural speech tracking benefit from lip movements correlate with worse performance when visual speech is blocked by a mask.

The paper introduces SparrKULee, a large EEG dataset of speech-evoked auditory responses from 85 participants, suitable for training machine learning models and benchmarking encoding/decoding tasks.

This study investigates neural tracking of acoustic and linguistic speech representations in individuals with post-stroke aphasia and finds decreased neural tracking of acoustic speech properties compared to healthy controls.

This study uses EEG and deep learning to classify native versus foreign speech perception based on linguistic features and observes differences in neural tracking patterns.

The paper introduces Eelbrain, a Python toolkit for time-continuous analysis using temporal response functions, demonstrating its application to EEG data from audiobook listening.

The paper uses a convolutional neural network to analyze EEG data and shows it better captures linguistic speech features than linear models.

This study investigates neural tracking of speech comprehension by comparing comprehensible and incomprehensible stories and a word list, finding that language tracking, not acoustic tracking, reflects speech comprehension.

This paper reviews neural tracking as a framework to assess auditory and linguistic processing during speech perception, highlighting three analyses: f0-tracking, envelope tracking, and linguistic feature tracking.

The paper investigates how speech understanding affects acoustic and linguistic neural tracking by manipulating speech rate, finding that acoustic tracking increases while linguistic tracking decreases with higher speech rates.

This study examines how aging affects neural tracking of speech processing, finding declines in linguistic processing and changes in latency and hemisphere-specific acoustic tracking.

This study shows that hearing loss is linked to delayed neural responses to continuous speech, with reduced efficiency in processing speech in quiet.

The paper evaluates linguistic representations as neural markers of speech comprehension using EEG, finding that phoneme surprisal, cohort entropy, word surprisal, and word frequency are tracked beyond acoustic properties.