Anne‐Lise Giraud

The paper shows top-down semantic predictions align phonetic neuronal dynamics in the human superior temporal gyrus during speech processing.

The paper investigates hierarchical syntactic planning in speech production using SEEG recordings, finding that core syntactic categories are planned before modifiers and that the hippocampus contributes to sentence structure building.

The paper shows that theta-gamma rhythms in human auditory cortex reflect speech acoustics, with distinct neural populations processing different speech timescales.

The paper examines multi-timescale functional connectivity dynamics in the brain using EEG and fMRI data, finding spatial convergence but temporal divergence across different FC timescales.

This study uses propofol sedation to enable non-invasive auditory EEG recording in unrestrained non-human primates, showing it allows high-quality data collection with minimal stress.

This study shows that training improves BCI control through changes in EEG power and neural features, suggesting the need for both machine and human learning in BCI development.

This study examines the consistency of correlations between fMRI resting-state networks and EEG band-power across different datasets and conditions, finding consistent correlations with variations based on RSN, frequency-band, and HRF delay.

The paper introduces a diffusion-approximation framework to model and interpret neuronal spiking dynamics using nonlinear Hawkes processes, showing its accuracy and applicability to human and nonhuman primate data.

This study investigates speech processing in children with ASD and finds reduced auditory response encoding and disrupted audio-visual temporal coordination.

This paper introduces BRyBI, a brain-rhythm-based model that integrates rhythmic processes to simulate speech comprehension, showing it can handle speech variations and aligns with human experiments.

This study investigates how dogs' sensory-motor tuning influences their ability to comprehend human speech, finding that their comprehension relies on a slower speech rhythm than humans.

This study identifies atypical neural oscillation coupling in young children with ASD that predicts speech reception difficulties.

This paper introduces a model that uses hierarchical predictions to assign semantic roles in speech comprehension, showing how nonlinguistic context helps disambiguate word meanings.

The paper shows that beta rhythm modulates prediction error precision in a speech recognition model, enhancing performance with optimal beta rate and alternating bottom-up/top-down processing.

This study links interindividual differences in aversion to rough sounds with neural excitability at 40 Hz, showing correlations with anxiety levels and suggesting 40 Hz sounds as neuromarkers for conditions like autism and schizophrenia.

This study shows that EEG and fMRI connectomes are reproducibly correlated across different simultaneous EEG-fMRI setups, including varying magnetic field strengths and electrode densities.

The paper decodes imagined speech using low- and cross-frequency neural features from electrocorticography data, showing their effectiveness in perceptual speech spaces.

The paper investigates how audio-visual syllable integration occurs, showing that combination results from temporal processing of discrepant stimuli after STS convergence.

This study shows that tACS at 30 Hz enhances low-gamma activity in dyslexia, improving phonemic processing and reading accuracy.

The paper shows that EEG and fMRI connectivity dynamics are linked, with synchronization in all oscillation bands reconfiguring at infraslow speeds and correlating with fMRI connectivity changes.

The paper investigates dual timescale processing in human auditory cortex using EEG, modeling, and decoding, finding bimodal spectral and informational patterns consistent with a dual subnetwork model.

The paper shows that representing speech sound categories at different time scales improves models of rapid recalibration compared to single-time-scale representations.

This paper identifies two spatially independent functional connectivity components across EEG and fMRI data, revealing linked but distinct connectivity dynamics across timescales.

This paper introduces a coherence-based spectro-spatial filter to predict stimulus features from electrocorticographic recordings, showing improved performance over traditional methods.

This study combines predictive coding with neural oscillations to improve on-line speech processing by aligning gamma activity with syllable boundaries and predictions with speech input.

The paper shows that speech sound categorization relies on focal neural activity in the temporal cortex, while distributed activity reflects collateral processes.