Ashesh D. Mehta

The paper investigates how transient high-frequency neural events contribute to sensory representation by analyzing their temporal, spectral, and phase properties across trials.

This study uses resting-state fMRI to predict seizure onset zones in drug-resistant epilepsy patients, achieving high accuracy in identifying epileptogenic regions non-invasively.

The paper identifies activity-dependent gene programs in human epileptic brain tissue and finds that some responses are conserved with acute stimulation, while others suggest metabolic and inflammatory dysregulation in epilepsy.

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.

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

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

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

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.

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.

The paper investigates neural activity during auditory conscious perception and finds similar cortical and subcortical network patterns to those observed in visual perception.

The paper shows that hippocampal and cortical high frequency oscillations (HFOs) increase at event boundaries and reoccur during recall, supporting a replay mechanism for memory consolidation.

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

The paper shows that hippocampal sharp wave ripples and cortical ripples coordinate semantic network reactivation during human free recall.

The paper uses intracranial EEG to identify motor-independent evidence accumulation dynamics in multiple human brain regions during perceptual decision tasks.

The paper shows that neuronal pattern similarities in human visual cortex remain stable during prolonged stimulation despite reduced activation magnitudes, suggesting perceptual content is linked to pattern profiles rather than activation levels.

The paper examines how semantic novelty modulates neural responses to visual changes like film cuts and saccades across the human brain using intracranial recordings.

The paper shows that saccades modulate excitability in auditory cortical areas during natural viewing, similar to visual areas, suggesting a role in improving information processing.

This study evaluates the neurophysiological effects of intracranial theta burst stimulation in the human sensorimotor cortex and finds increased beta band synchronization indicative of neuroplasticity.

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 identifies face-selective neurons in human ventral temporal cortex that reactivate during free recall of faces, suggesting a role in memory retrieval.

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

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

This study shows that measuring neural activity during repetitive electrical stimulation can predict post-stimulation changes in brain network connectivity.

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

The paper shows that face-selective brain columns match deep convolutional networks in intermediate layers, suggesting a role in pictorial face representation rather than person identification.

This study shows that repeated performance in complex visual problem-solving tasks leads to reduced cortical responses in specific brain regions, but not changes in response time.

The paper shows that saccade-related transients are suppressed in higher-level visual cortex but not early visual cortex in humans.

iELVis localizes and visualizes intracranial electrodes using MRI and neuroimaging tools, providing accurate brain shift correction and interactive 3D/2D visualization with functional overlays.