Amirhossein Khalilian-Gourtani

The paper identifies a modality-specific gradient in the frontal cortex during naturalistic audiovisual processing, showing dynamic adaptation to different contexts.

The paper uses graph-theoretical connectivity from ECoG to predict eloquent cortex during presurgical mapping, finding varying accuracy across functional domains.

This study investigates the neural dynamics of automatic speech production using ECoG recordings, revealing a distributed cortical network with complex feedforward and feedback signal representation.

Mithra is a cross-platform visualization toolkit for intracranial recordings that enables systematic within- and across-subject electrode localization and analysis.

The paper decodes behavioral states during speech production and perception using functional connectivity from ECoG data, achieving 73.9% accuracy in classifying states.

The paper creates a probabilistic functional atlas of brain regions using extraoperative electrocortical stimulation data, revealing variability in functional organization and highlighting key areas for language and motor functions.

The paper identifies the source and timing of a corollary discharge signal in human speech using ECoG recordings and Granger-causality analysis.

This paper introduces a deep learning framework for decoding speech from neural signals, achieving high correlation with original speech spectrograms and providing open-source code.

This paper introduces a transformer-based model for speech decoding from neural signals using surface and depth electrodes, achieving high performance across multiple participants and electrode types.

This study uses a deep learning model to decode speech from neural signals, revealing that frontal and temporal cortex process both feedforward and feedback information during speech production.