Zitong Lu

Coordinated Drift of Image Representations in Human EEG

The paper investigates drift in human EEG responses to images across sessions, finding coordinated drift along image identity dimensions and preserved global structure.

AVMoments-EEG: A Large-Scale EEG Dataset of Naturalistic Audiovisual Event Perception

AVMoments-EEG provides a large-scale EEG dataset for studying naturalistic audiovisual event perception with high temporal resolution and systematic experimental manipulation.

Img2EEG: A Scalable and Interpretable Encoding Framework for Simulating Human EEG Responses to Visual Inputs

Img2EEG generates synthetic EEG responses to visual inputs, generalizing across stimuli and participants while preserving neural structure and enabling targeted interventions.

Distinct mitochondrial phenotypes align with visual and semantic representations across human cortex

The paper links visual and semantic representations in human cortex to distinct mitochondrial phenotypes and transcriptomic profiles.

Shared and distinct temporal representations of Chinese words across imaged, silent, and overt speech

The paper investigates how neural representations of Chinese words differ across imagined, silent, and overt speech, finding shared and distinct temporal dynamics and representational geometries.

Face-like holistic Processing in Non-Face Stimuli

This study shows that holistic processing occurs when stimuli rely on configural information, not featural cues, and correlates with face recognition ability.

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

This study uses human EEG and artificial neural networks to disentangle neural representations of object real-world size, depth, and retinal size, revealing a processing timeline and stable higher-level representation of real-world size.

Unfolding spatiotemporal representations of 3D visual perception in the human brain

This study investigates how the human brain encodes 3D visual space by analyzing neural representations across time and brain regions using a multimodal dataset and computational methods.

Dynamic saccade context triggers more stable object-location binding

The paper investigates how dynamic saccade context affects object-location binding, finding that it promotes spatiotopic binding rather than retinotopic binding.

Bridging the Gap: Exploring Neural Mechanisms of Facial Repetition Suppression through EEG and DCNNs

This study compares EEG brain activation patterns with DCNNs to explore neural mechanisms of facial repetition suppression, finding greater similarity with the Fatigue model.

NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data

NeuroRA is a Python toolbox for representational analysis of multi-modal neural data, enabling cross-modal comparisons and various similarity calculations.