Tom Francart

This study disentangles attention and listening engagement by manipulating both factors and finds that neural synchronization predicts attention while bodily and self-report measures predict engagement.

The paper investigates neural speech tracking using EEG to quantify spatial release from masking in normal-hearing listeners, finding that spatial separation enhances neural tracking and reflects behavioral improvements.

This study evaluates the impact of neurofeedback on auditory attention decoding accuracy and user experience in a closed-loop system, finding that auditory feedback can degrade performance and increase cognitive load.

This paper evaluates auditory attention decoding in conversation-tracking scenarios, finding that accuracy is not affected by attention direction or switches, and correlates with perceived listening effort.

This study uses large language models to analyze how attention affects neural tracking of linguistic context in multi-talker speech environments.

This study examines how speaker and listener sex affect auditory attention decoding performance using EEG and EOG measurements in young adults.

This study evaluates DNN-generated virtual humans for enhancing audiovisual speech perception in noise using behavioral and neural measures, finding they improve speech intelligibility compared to human speakers.

This study compares neural tracking and spectral entropy in DoC patients to assess their brain responses to speech, finding correlations with clinical diagnosis and behavioral responsiveness.

This study shows that interpersonal synchronization of brain, heart, and skin responses correlates with subjective listening engagement during story listening.

This study uses EEG to identify altered brain network patterns in chronic aphasia during natural speech listening, finding distinct connectivity differences and potential biomarkers.

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.

This study compares single and multiplexed amplitude-modulated stimuli for auditory steady-state responses, finding that single stimuli yield higher signal-to-noise ratios but multiplexed stimuli reduce recording time.

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 paper decodes absolute auditory attention using EEG features, finding that neural envelope tracking is more reliable than spectral entropy for distinguishing active listening from distractor conditions.

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.

This study investigates neural envelope tracking in post-stroke aphasia, finding that lesions in language areas like the middle temporal gyrus are associated with poorer tracking, and that tracking relates to auditory and language skills.

This study investigates biases in EEG-based spatial auditory attention decoding, finding that simple models can overfit to eye-gaze and trial-specific confounds, leading to inflated accuracy.

This study shows neural envelope tracking can predict speech intelligibility and hearing aid benefit in children with hearing loss, correlating with behavioral measures and showing improved tracking with hearing aids.

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 study shows that delta-band neural envelope tracking correlates with speech intelligibility in noise in preschoolers, offering an objective measure for assessing speech in difficult-to-test populations.

This paper compares linear and mutual information analyses of EEG data to study neural envelope tracking, finding that mutual information, particularly using Gaussian copula, better captures nonlinear speech processing.

This paper introduces a deep neural network called VLAAI for decoding speech envelopes from EEG, showing improved performance over linear models and demonstrating good generalization across subjects and stimuli.

This study shows individuals with aphasia have impaired acoustic and phonemic processing, with 76% showing low-level impairments that predict higher-level language processing difficulties.

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 paper introduces a time-adaptive unsupervised method for auditory attention decoding using EEG, showing it outperforms a time-invariant supervised approach.

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