Zhengjia Wang

This paper presents a functional atlas of the human central nervous system using high-resolution surface electrophysiology from 70 patients, revealing conserved electrophysiological patterns and enabling neuroprosthetic applications.

The paper introduces three R packages to create an open-source ecosystem for seizure localization using iEEG data, focusing on the Neural Fragility method and reproducibility.

The paper demonstrates that a deep neural network model, AVHuBERT, can replicate the McGurk effect, showing similar perceptual responses to humans when exposed to audiovisual speech stimuli.

This paper develops a machine learning model to identify seizure onset zones using iEEG data and predicts post-operative seizure freedom, achieving high accuracy in both tasks.

YAEL streamlines electrode localization in iEEG by integrating all steps into a single GUI, enabling efficient and automated localization with high-quality visualization.

The paper uses intracranial EEG to analyze spectral features in the dorsal anterior cingulate cortex, revealing hemispheric specialization for valence and intensity processing and showing causal effects of stimulation on emotional face perception.

RAVE is an open-source software for analyzing and visualizing intracranial EEG data, enabling users to perform custom analyses and visualize results without coding.

This paper uses iEEG deconvolution to study neural responses to visual speech in human posterior superior temporal gyrus, finding subadditive multisensory processing and visual enhancement of auditory speech.