Jérémie Mattout

This study uses MEG and machine learning to decode spatial attention in humans during different tasks, showing dynamic, task-dependent attentional patterns similar to those in non-human primates.

The paper compares EEG, SQUID-MEG, and 4Helium-OPMs in measuring auditory mismatch negativity using a passive oddball paradigm, finding comparable performance across all modalities.

This paper introduces a multilayer MEG source modelling framework to enable depth-resolved laminar inference in humans, showing it aligns with known neural circuit motifs.

The paper investigates beta burst activity in motor imagery tasks, showing that burst-based features can match or exceed traditional beta power methods in decoding brain signals.

This paper improves motor imagery-based BCI by analyzing beta burst waveforms instead of beta power, showing better classification performance and information transfer rates.

The paper uses computational models to analyze EEG data and finds that adaptation and Bayesian learning mechanisms explain different temporal phases of the MMN response.

The paper investigates how humans adapt their sense of agency when consequences precede their actions in an AI-assisted environment, finding implicit learning occurs within an hour.

The paper identifies diverse beta burst waveform motifs in human MEG data during movement tasks and shows they reflect distinct computational processes.

The paper investigates how brain responses to expected and unexpected sounds are modulated by precision and prediction error, revealing adaptive learning mechanisms in auditory processing.

This study improves MEG source inversion by using white matter surface locations and correspondence-based orientation estimation, showing better performance in evoked response fitting.

The paper compares visual and automatic sleep scoring methods to assess their reliability and variability in large datasets, finding that automatic scoring can be a reliable reference when reproducible.

The paper evaluates fused EEG-MEG source reconstruction using real data, finding it improves spatial localization compared to unimodal methods for auditory mismatch responses.