Alain De Cheveigné A

This paper reviews spatial filters and their limitations in brain data analysis, focusing on virtual electrodes and their practical constraints.

This paper introduces Rapid FFR, a method that improves FFR recording efficiency by using continuous stimulation and cycle averaging, showing higher signal quality and reliability compared to conventional methods.

This paper presents a real-time BCI system using EEG attention decoding to control a hearing instrument, enhancing speech perception in noisy environments by steering attention to desired sound sources.

This paper introduces a method to infer brain activity sources and their locations from electromagnetic signals using linear analysis and anatomical models.

This paper discusses a feed-forward predictive coding model in the auditory brainstem that minimizes prediction error to create a sensory representation invariant to certain masking sounds.

This paper re-evaluates the benefit of EEG pre-processing using the same data as a previous study, arguing that the metric used has limited applicability and pre-processing may be critical in some cases.

The paper presents a method to generate auditory-nerve spike trains by applying a time- and interval-dependent thinning process to a Poisson process, enabling efficient simulation of neural auditory perception.

The paper explains why listeners tolerate positive mistuning more than negative mistuning in an octave pair by proposing a neural circuit with mismatched time constants.

The paper proposes a model of early auditory processing using delay-and-subtract cancellation filters to explain masking asymmetry between pure tones and noise.

This paper introduces a denoising algorithm for multichannel data that identifies and removes noise by analyzing local subspaces, improving upon linear methods that fail when sources exceed channels.

The paper introduces a match-mismatch task to evaluate auditory stimulus-response models, showing state-of-the-art performance and providing a reference implementation.

This paper introduces Shared Component Analysis (SCA) as an alternative to PCA for neuroimaging data dimensionality reduction, prioritizing components shared across multiple sensors.

The paper examines how audiovisual speech integration varies with listening conditions, finding enhanced phonetic-level integration in noisy environments.

This study shows that cortical responses to music encode melodic expectations, with neural signals better predicted by combining acoustic and melodic features than either alone.

The paper introduces a method to remove power line artifacts from electrophysiological data by combining spectral and spatial filtering, evaluated on synthetic and real data.

The paper improves stimulus-response models for brain responses to speech using adaptive techniques and recurrent neural networks, achieving better classification performance in a match-vs-mismatch task.

This study compares TRF estimation methods for decoding auditory attention from EEG data using a standardized dataset.

The paper examines how the brain processes phonotactic probabilities in speech using EEG, finding that these probabilities modulate neural responses and support a pre-lexical role in speech perception.

This paper reviews multiway canonical correlation analysis (MCCA) for fusing brain signals across subjects to extract common components, with examples and discussion of its limitations.

The paper introduces techniques for preprocessing multichannel data to remove artifacts like drift, glitches, and ringing, improving data quality without discarding corrupted trials.

This paper introduces Canonical Correlation Analysis (CCA) to decode auditory brain responses and improve stimulus-response mapping for BCI applications.

This paper develops a low-parameter model to study firing regularity in chopper cells of the ventral cochlear nucleus, showing multiple mechanisms can explain their behavior and predicting effects of synaptopathy.