Fengyu Cong

TLS-Sim provides a toolkit for individualized transcranial light stimulation modeling using deep-learning-accelerated Monte Carlo simulations to improve efficiency and accuracy.

The paper investigates semantic matching across word and picture formats using EEG decoding and temporal generalization analyses, finding distinct neural dynamics for each format.

This paper introduces a subject-level searchlight optimization framework combining univariate and multivariate analyses to identify condition-sensitive voxels for emotional state decoding in fMRI.

This study evaluates how downsampling affects ERP data quality, univariate metrics, and multivariate decoding, finding that lower sampling rates impact univariate measures more than decoding performance.

This study explores using real-time representational similarity analysis in fMRI neurofeedback to navigate emotional states, showing feasibility and improved performance with repeated sessions.

The paper evaluates how electrode density affects EEG decoding and RSA across three cognitive tasks, finding task-dependent impacts and suggesting low-density setups can be sufficient.

The paper examines how harmonious and disharmonious color combinations affect neural activity and aesthetic judgments using EEG data.

This paper introduces a dual-projection based ICA denoising method to remove scanner/site effects in multi-site MRI data, showing improved performance over traditional ICA and GLM methods.

The paper applies single-trial-based temporal principal component analysis to extract event-related potentials from individual subject data, showing improved consistency with fewer trials.

This paper introduces a tensor component analysis framework to identify shared brain networks across subjects during naturalistic stimuli, improving reproducibility and capturing individual differences.

This study identifies oscillatory hyperconnectivity and hypoconnectivity networks in major depression using coupled tensor decomposition and music-induced EEG data.

This paper introduces a data-driven method to objectively extract evoked event-related oscillations from ERP data, showing improved detection compared to conventional methods.