Xing Tian

The paper investigates the state-dependent coupling between oscillatory and aperiodic neural activity using intracranial recordings and a dynamical model, finding that local inhibitory feedback drives this coupling.

The paper examines how auditory tracking changes during wakefulness and propofol anesthesia by analyzing response magnitude, temporal alignment, and anatomical distribution using sEEG data.

The paper identifies distinct fast and slow temporal components in human brain activity using SPLIT on intracranial EEG data, showing they follow different organizational principles.

This study compares human and artificial speech comprehension by analyzing local sequential phonology-to-semantics transfer mechanisms in both.

This study shows that rhythmic motor activity can enhance auditory attention during attentional blinks but impair performance when attention is sufficient.

The paper reports shared neural responses to morphological and syntactic structure complexity in Mandarin Chinese, supporting a non-lexicalist view of language comprehension.

The paper investigates the role of consciousness in binding basic sensory features using sEEG recordings in awake and anesthetized states, finding that consciousness is necessary for feature binding but not for encoding individual attributes.

This paper investigates the neural coding of articulatory dynamics during speech imagery and production, revealing a hierarchical spatiotemporal organization and developing a model to decode speech from neural signals.

The paper investigates whether mental objects from non-visual sources share the same neural representation as visual ones, finding similar alpha power suppression patterns.

The paper investigates how motor compensation, not sensory error, drives serial learning in speech production through experimental designs and behavioral analysis.

The paper investigates how impaired motor-to-sensory transformation contributes to auditory hallucinations by analyzing EEG data from schizophrenic patients with and without hallucinations.

The paper identifies a common frontal-temporal network for motor synchronization to auditory and visual rhythms, showing it is based on intrinsic cortical connections rather than sensory experience.

This study uses ERP to investigate how events and their components are represented in working memory, finding distinct neural patterns for event relations and roles.

This study shows that attention distribution during goal-directed reading is predicted by transformer-based DNNs optimized for the same task.

This study shows that manual gestures enhance lexical stress learning by modulating auditory neural responses, with effects dependent on the alignment of gesture and speech features.

This study identifies two distinct neural networks mediating top-down sensory prediction through motor and memory systems using fMRI and a dual imagery paradigm.

The paper investigates how multi-scale predictions modulate tone perception in schizophrenia patients with auditory verbal hallucinations, finding distinct effects in patients versus healthy controls.

The paper decodes imagined speech using low- and cross-frequency neural features from electrocorticography data, showing their effectiveness in perceptual speech spaces.

The paper investigates how motor-to-sensory neural transformations support imagined singing and verbal thinking by analyzing MEG data during rhythmic mental tasks.

This study compares semantic representations in neural and computational systems using EEG and finds significant correlations between model outputs and early semantic processing.

The paper investigates how different motor signals during speech preparation modulate auditory responses, finding that preparation without linguistic content suppresses all speech sounds, while preparing a syllable enhances responses to that syllable.

This study investigates the role of insular and supra-temporal auditory areas in humans using intracranial recordings and stimulation, finding that while insula has similar auditory responses, it does not directly contribute to auditory perception.

The paper develops a framework for analyzing neural dynamics in EEG data using topographic analysis, providing automated temporal indices and reducing cognitive noise.