Jill Kries

All Publications

• Measuring naturalistic speech comprehension in real time. Behavior research methods Ergin, I., Kries, J., Gupta, S., Papworth Burrel, M., Gwilliams, L. 2026; 58 (4)

  Abstract

  Speech comprehension has been described as an effortless and robust process; yet, in real-world contexts, it is common for a listener to misunderstand what was said or fail to derive meaning entirely. Typically, methods of measuring speech comprehension are applied 'post hoc' - that is, after the comprehension has happened. This approach fails to capture comprehension as it occurs, limiting the field's understanding of the cognitive processes involved in real-time comprehension. To overcome these challenges, we designed and tested a novel method of measuring real-time speech comprehension during naturalistic listening. We built a slider device that synchronizes with experimental software and provides millisecond read-out. In three experiments, participants listened to audiobook segments while providing continuous comprehension ratings using the slider. To vary comprehension success, we presented speech segments at speed factors of 1-5 times faster than normal. We validated the time-resolved slider data against established speech comprehension assessment methods. Overall, our findings validate our novel time-resolved comprehension measure and demonstrate that it is possible to derive an online behavioral measure of real-time speech comprehension. We also confirmed numerous limitations of static post hoc assessments, including challenges with multiple-choice question design and the confounding of potential effects due to recency bias and comprehension for summarization. The measure proposed here overcomes the constraints of static post hoc assessments and can be effectively integrated with neuroimaging techniques, offering a valuable tool for future research on dynamic processes during naturalistic listening.

  View details for DOI 10.3758/s13428-026-02941-1

  View details for PubMedID 41896392

  View details for PubMedCentralID PMC13031255

• The spatio-temporal dynamics of phoneme encoding in aging and aphasia. The Journal of neuroscience : the official journal of the Society for Neuroscience Kries, J., Vandermosten, M., Gwilliams, L. 2025

  Abstract

  During successful language comprehension, speech sounds (phonemes) are encoded within a series of neural patterns that evolve over time. Here we tested whether these neural dynamics of speech encoding are altered for individuals with a language disorder. We recorded EEG responses from human brains of 39 individuals with post-stroke aphasia (13♀/26♂) and 24 healthy age-matched controls (i.e., older adults; 8♀/16♂) during 25 minutes of natural story listening. We estimated the duration of phonetic feature encoding, speed of evolution across neural populations, and the spatial location of encoding over EEG sensors. First, we establish that phonetic features are robustly encoded in EEG responses of healthy older adults. Second, when comparing individuals with aphasia to healthy controls, we find significantly decreased phonetic encoding in the aphasic group after shared initial processing pattern (0.08-0.25s after phoneme onset). Phonetic features were less strongly encoded over left-lateralized electrodes in the aphasia group compared to controls, with no difference in speed of neural pattern evolution. Finally, we observed that healthy controls, but not individuals with aphasia, encode phonetic features longer when uncertainty about word identity is high, indicating that this mechanism - encoding phonetic information until word identity is resolved - is crucial for successful comprehension. Together, our results suggest that aphasia may entail failure to maintain lower-order information long enough to recognize lexical items.Significance statement This study reveals robust decoding of speech sound properties, so-called phonetic features, from EEG recordings in older adults, as well as decreased phonetic processing in individuals with a language disorder (aphasia) compared to healthy controls. This was most prominent over left-hemispheric electrodes. Additionally, we observed that healthy controls, but not individuals with aphasia, encode phonetic features longer when uncertainty about word identity is high, indicating that this mechanism - encoding phonetic information until word identity is resolved - is crucial for successful language processing. These insights deepen our understanding of disrupted mechanisms in a language disorder, and show how the integration between language processing levels works in the healthy aging, neurotypical brain.

  View details for DOI 10.1523/JNEUROSCI.1001-25.2025

  View details for PubMedID 41461535

• Neural tracking of natural speech: an effective marker for post-stroke aphasia BRAIN COMMUNICATIONS De Clercq, P., Kries, J., Mehraram, R., Vanthornhout, J., Francart, T., Vandermosten, M. 2025; 7 (2): fcaf095

  Abstract

  After a stroke, approximately one-third of patients suffer from aphasia, a language disorder that impairs communication ability. Behavioural tests are the current standard to detect aphasia, but they are time-consuming, have limited ecological validity and require active patient cooperation. To address these limitations, we tested the potential of EEG-based neural envelope tracking of natural speech. The technique investigates the neural response to the temporal envelope of speech, which is critical for speech understanding by encompassing cues for detecting and segmenting linguistic units (e.g. phrases, words and phonemes). We recorded EEG from 26 individuals with aphasia in the chronic phase after stroke (>6 months post-stroke) and 22 healthy controls while they listened to a 25-min story. We quantified neural envelope tracking in a broadband frequency range as well as in the delta, theta, alpha, beta and gamma frequency bands using mutual information analyses. Besides group differences in neural tracking measures, we also tested its suitability for detecting aphasia at the individual level using a support vector machine classifier. We further investigated the reliability of neural envelope tracking and the required recording length for accurate aphasia detection. Our results showed that individuals with aphasia had decreased encoding of the envelope compared to controls in the broad, delta, theta and gamma bands, which aligns with the assumed role of these bands in auditory and linguistic processing of speech. Neural tracking in these frequency bands effectively captured aphasia at the individual level, with a classification accuracy of 83.33% and an area under the curve of 89.16%. Moreover, we demonstrated that high-accuracy detection of aphasia can be achieved in a time-efficient (5-7 min) and highly reliable manner (split-half reliability correlations between R = 0.61 and R = 0.96 across frequency bands). In this study, we identified specific neural response characteristics to natural speech that are impaired in individuals with aphasia, holding promise as a potential biomarker for the condition. Furthermore, we demonstrate that the neural tracking technique can discriminate aphasia from healthy controls at the individual level with high accuracy, and in a reliable and time-efficient manner. Our findings represent a significant advance towards more automated, objective and ecologically valid assessments of language impairments in aphasia.

  View details for DOI 10.1093/braincomms/fcaf095

  View details for Web of Science ID 001440166900001

  View details for PubMedID 40066108

  View details for PubMedCentralID PMC11891514

• EEG reveals brain network alterations in chronic aphasia during natural speech listening. Scientific reports Mehraram, R., Kries, J., De Clercq, P., Vandermosten, M., Francart, T. 2025; 15 (1): 2441

  Abstract

  Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. EEG was recorded from 22 HC and 27 PWA while they listened to a 25-min-long story. Functional connectivity between scalp regions was measured with the weighted phase lag index. The Network-Based Statistics toolbox was used to detect altered network patterns and to investigate correlations with behavioural tests within the aphasia group. Differences in network geometry were assessed by means of graph theory and a targeted node-attack approach. Group-classification accuracy was obtained with a support vector machine classifier. PWA showed stronger inter-hemispheric connectivity compared to HC in the theta-band (4.5-7 Hz), whilst a weaker subnetwork emerged in the low-gamma band (30.5-49 Hz). Two subnetworks correlated with semantic fluency in PWA respectively in delta- (1-4 Hz) and low-gamma-bands. In the theta-band network, graph alterations in PWA emerged at both local and global level, whilst only local changes were found in the low-gamma-band network. Network metrics discriminated PWA and HC with AUC = 83%. Overall, we demonstrate the potential of EEG-network metrics for the development of informative biomarkers to assess natural speech processing in chronic aphasia. We hypothesize that the detected alterations reflect compensatory mechanisms associated with recovery.

  View details for DOI 10.1038/s41598-025-86192-8

  View details for PubMedID 39828755

  View details for PubMedCentralID PMC11743778

• Functional connectivity of stimulus-evoked brain responses to natural speech in post-stroke aphasia JOURNAL OF NEURAL ENGINEERING Mehraram, R., De Clercq, P., Kries, J., Vandermosten, M., Francart, T. 2024; 21 (6)

  Abstract

  Objective. One out of three stroke-patients develop language processing impairment known as aphasia. The need for ecological validity of the existing diagnostic tools motivates research on biomarkers, such as stimulus-evoked brain responses. With the aim of enhancing the physiological interpretation of the latter, we used EEG to investigate how functional brain network patterns associated with the neural response to natural speech are affected in persons with post-stroke chronic aphasia.Approach. EEG was recorded from 24 healthy controls and 40 persons with aphasia while they listened to a story. Stimulus-evoked brain responses at all scalp regions were measured as neural envelope tracking in the delta (0.5-4 Hz), theta (4-8 Hz) and low-gamma bands (30-49 Hz) using mutual information. Functional connectivity between neural-tracking signals was measured, and the Network-Based Statistics toolbox was used to: (1) assess the added value of the neural tracking vs EEG time series, (2) test between-group differences and (3) investigate any association with language performance in aphasia. Graph theory was also used to investigate topological alterations in aphasia.Main results. Functional connectivity was higher when assessed from neural tracking compared to EEG time series. Persons with aphasia showed weaker low-gamma-band left-hemispheric connectivity, and graph theory-based results showed a greater network segregation and higher region-specific node strength. Aphasia also exhibited a correlation between delta-band connectivity within the left pre-frontal region and language performance.Significance.We demonstrated the added value of combining brain connectomics with neural-tracking measurement when investigating natural speech processing in post-stroke aphasia. The higher sensitivity to language-related brain circuits of this approach favors its use as informative biomarker for the assessment of aphasia.

  View details for DOI 10.1088/1741-2552/ad8ef9

  View details for Web of Science ID 001355711300001

  View details for PubMedID 39500050

• Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference De Clercq, P., Puffay, C., Kries, J., Van Hamme, H., Vandermosten, M., Francart, T., Vanthornhout, J. 2024; 2024: 1-5

  Abstract

  Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavioral language tests. However, these tests are time-consuming, require manual interpretation by trained clinicians, suffer from low ecological validity, and diagnosis can be biased by comorbid motor and cognitive problems present in aphasia. In this study, we introduce an automated screening tool for speech processing impairments in aphasia that relies on time-locked brain responses to speech, known as neural tracking, within a deep learning framework. We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech. Subsequently, we evaluated our models on an independent sample comprising 26 individuals with aphasia (IWA) and 22 healthy controls. Our results reveal decreased tracking of all speech representations in IWA. Utilizing a support vector machine classifier with neural tracking measures as input, we demonstrate high accuracy in aphasia detection at the individual level (85.42%) in a time-efficient manner (requiring 9 minutes of EEG data). Given its high robustness, time efficiency, and generalizability to unseen data, our approach holds significant promise for clinical applications.

  View details for DOI 10.1109/EMBC53108.2024.10781830

  View details for PubMedID 40039757

• Exploring neural tracking of acoustic and linguistic speech representations in individuals with post-stroke aphasia. Human brain mapping Kries, J., De Clercq, P., Gillis, M., Vanthornhout, J., Lemmens, R., Francart, T., Vandermosten, M. 2024; 45 (8): e26676

  Abstract

  Aphasia is a communication disorder that affects processing of language at different levels (e.g., acoustic, phonological, semantic). Recording brain activity via Electroencephalography while people listen to a continuous story allows to analyze brain responses to acoustic and linguistic properties of speech. When the neural activity aligns with these speech properties, it is referred to as neural tracking. Even though measuring neural tracking of speech may present an interesting approach to studying aphasia in an ecologically valid way, it has not yet been investigated in individuals with stroke-induced aphasia. Here, we explored processing of acoustic and linguistic speech representations in individuals with aphasia in the chronic phase after stroke and age-matched healthy controls. We found decreased neural tracking of acoustic speech representations (envelope and envelope onsets) in individuals with aphasia. In addition, word surprisal displayed decreased amplitudes in individuals with aphasia around 195ms over frontal electrodes, although this effect was not corrected for multiple comparisons. These results show that there is potential to capture language processing impairments in individuals with aphasia by measuring neural tracking of continuous speech. However, more research is needed to validate these results. Nonetheless, this exploratory study shows that neural tracking of naturalistic, continuous speech presents a powerful approach to studying aphasia.

  View details for DOI 10.1002/hbm.26676

  View details for PubMedID 38798131

• Acoustic and phonemic processing are impaired in individuals with aphasia SCIENTIFIC REPORTS Kries, J., De Clercq, P., Lemmens, R., Francart, T., Vandermosten, M. 2023; 13 (1): 11208

  Abstract

  Acoustic and phonemic processing are understudied in aphasia, a language disorder that can affect different levels and modalities of language processing. For successful speech comprehension, processing of the speech envelope is necessary, which relates to amplitude changes over time (e.g., the rise times). Moreover, to identify speech sounds (i.e., phonemes), efficient processing of spectro-temporal changes as reflected in formant transitions is essential. Given the underrepresentation of aphasia studies on these aspects, we tested rise time processing and phoneme identification in 29 individuals with post-stroke aphasia and 23 healthy age-matched controls. We found significantly lower performance in the aphasia group than in the control group on both tasks, even when controlling for individual differences in hearing levels and cognitive functioning. Further, by conducting an individual deviance analysis, we found a low-level acoustic or phonemic processing impairment in 76% of individuals with aphasia. Additionally, we investigated whether this impairment would propagate to higher-level language processing and found that rise time processing predicts phonological processing performance in individuals with aphasia. These findings show that it is important to develop diagnostic and treatment tools that target low-level language processing mechanisms.

  View details for DOI 10.1038/s41598-023-37624-w

  View details for Web of Science ID 001033319900047

  View details for PubMedID 37433805

  View details for PubMedCentralID PMC10336064

• Neural tracking of linguistic and acoustic speech representations decreases with advancing age NEUROIMAGE Gillis, M., Kries, J., Vandermosten, M., Francart, T. 2023; 267: 119841

  Abstract

  Older adults process speech differently, but it is not yet clear how aging affects different levels of processing natural, continuous speech, both in terms of bottom-up acoustic analysis and top-down generation of linguistic-based predictions. We studied natural speech processing across the adult lifespan via electroencephalography (EEG) measurements of neural tracking.Our goals are to analyze the unique contribution of linguistic speech processing across the adult lifespan using natural speech, while controlling for the influence of acoustic processing. Moreover, we also studied acoustic processing across age. In particular, we focus on changes in spatial and temporal activation patterns in response to natural speech across the lifespan.52 normal-hearing adults between 17 and 82 years of age listened to a naturally spoken story while the EEG signal was recorded. We investigated the effect of age on acoustic and linguistic processing of speech. Because age correlated with hearing capacity and measures of cognition, we investigated whether the observed age effect is mediated by these factors. Furthermore, we investigated whether there is an effect of age on hemisphere lateralization and on spatiotemporal patterns of the neural responses.Our EEG results showed that linguistic speech processing declines with advancing age. Moreover, as age increased, the neural response latency to certain aspects of linguistic speech processing increased. Also acoustic neural tracking (NT) decreased with increasing age, which is at odds with the literature. In contrast to linguistic processing, older subjects showed shorter latencies for early acoustic responses to speech. No evidence was found for hemispheric lateralization in neither younger nor older adults during linguistic speech processing. Most of the observed aging effects on acoustic and linguistic processing were not explained by age-related decline in hearing capacity or cognition. However, our results suggest that the effect of decreasing linguistic neural tracking with advancing age at word-level is also partially due to an age-related decline in cognition than a robust effect of age.Spatial and temporal characteristics of the neural responses to continuous speech change across the adult lifespan for both acoustic and linguistic speech processing. These changes may be traces of structural and/or functional change that occurs with advancing age.

  View details for DOI 10.1016/j.neuroimage.2022.119841

  View details for Web of Science ID 000923416200001

  View details for PubMedID 36584758

  View details for PubMedCentralID PMC9878439