Nima Mesgarani

Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems

This study compares the hierarchical processing of speech in the human auditory cortex with a recurrent ASR model, finding parallel representations across acoustic, phonetic, lexical, and semantic stages.

Neural Decoding of Musical Engagement Reveals Tension-Release Dynamics

This study uses EEG and ECoG to decode musical tension dynamics and their relationship with neural engagement during naturalistic music listening.

Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech

This study uses large language models to analyze how attention affects neural tracking of linguistic context in multi-talker speech environments.

Speaker Identity is Robustly Encoded in Spatial Patterns of Intracranial EEG for Attention Decoding

This paper shows that speaker identity is encoded in spatial patterns of intracranial EEG during attention, enabling a new method for auditory attention decoding.

Joint population coding and temporal coherence link an attended talker's voice and location features in naturalistic multi-talker scenes.

The paper shows that the auditory cortex uses joint population coding and temporal coherence to integrate voice and location features of attended talkers in multi-talker scenes.

The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models

This study uses electrophysiological recordings and generative music models to show that musicians have more structured and left-hemisphere biased neural encoding of music compared to non-musicians.

Evaluating scientific theories as predictive models in language neuroscience

This paper introduces QA encoding models that convert qualitative language theories into interpretable brain response predictors, outperforming baselines in fMRI and ECoG data.

Decoding the Unintelligible: Neural Speech Tracking in Low Signal-to-Noise Ratios

This study shows that neural speech tracking remains reliable in low signal-to-noise ratios, suggesting the brain retains sufficient information for decoding attended speech.

Distinct roles of SNR, speech Intelligibility, and attentional effort on neural speech tracking in noise

This study examines how signal-to-noise ratio, speech intelligibility, and attentional effort affect neural speech tracking in noise, finding distinct roles for each factor.

Temporal integration in human auditory cortex is predominantly yoked to absolute time, not structure duration

The paper investigates how human auditory cortex integrates sound structures over time versus structure duration, finding that integration is primarily time-yoked.

Brain-controlled augmented hearing for spatially moving conversations in multi-talker environments

This paper develops a brain-controlled assistive hearing system for multi-talker environments with moving conversations, improving speech intelligibility and tracking.

The Spatial Reach of Neuronal Coherence and Spike-field Coupling across the Human Neocortex

The paper measures the spatial reach of neuronal coherence and spike-field coupling in human neocortex during cognitive tasks, finding a consistent 2-3 cm effective range for local communication.

Functional characterization of human Heschl's gyrus in response to natural speech

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.

Neural representation of linguistic feature hierarchy reflects second-language proficiency

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.

Phase resetting in human auditory cortex to visual speech

The paper investigates how visual speech influences auditory cortex activity through phase resetting of neuronal oscillations during audiovisual speech processing.

Multiscale integration organizes hierarchical computation in human auditory cortex

The paper develops a method to estimate neural integration periods in human auditory cortex and shows integration periods increase with cortical hierarchy, correlating with selectivity for acoustic features and sound categories.

Does the phase of ongoing EEG oscillations predict auditory perception?

This study investigates whether the phase of ongoing EEG oscillations predicts auditory discrimination accuracy and finds a linear relationship between phase distribution divergence and performance.

Estimating and interpreting nonlinear receptive fields of sensory responses with deep neural network models

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.

Cortical encoding of melodic expectations in human temporal cortex

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.

Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods

This study compares two-talker attention decoding from EEG using nonlinear neural networks and linear methods, finding that a new architecture outperforms baselines and that wet and dry EEG systems yield comparable results.

Towards reconstructing intelligible speech from the human auditory cortex

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