Bahar Khalighinejad

This study uses intracranial EEG to analyze the functional organization of Heschl's gyrus during natural speech listening, revealing a gradient of acoustic and linguistic feature processing.

The paper examines how second-language proficiency affects neural encoding of linguistic features in non-native listeners using EEG, finding that proficiency leads to convergence in phonemic encoding with native speakers.

This paper introduces a deep neural network framework to model nonlinear sensory responses, showing improved prediction accuracy and revealing distinct nonlinear transformations in auditory regions.

This paper improves speech reconstruction from the human auditory cortex using deep learning and speech synthesis, achieving 65% better intelligibility than previous methods.