Linling Li

This study introduces DEFN, a novel approach using dynamic functional connectivity to identify emotion-specific neural signatures, improving classification accuracy for mood disorders.

This study develops an EEG neurofeedback-guided cognitive reappraisal training protocol for emotion regulation, showing improved performance for low positive valence stimuli with real-time feedback.

The paper introduces A-HER, a paradigm that maximizes auditory stimulus entropy to study brain processing of uncertain information, showing improved signal-to-noise ratio and repeatability compared to other methods.

This study performs a brain-wide GWAS to identify genetic loci associated with structural and functional brain phenotypes of pain sensitivity, finding candidate loci in the right insula and CYP2D6 gene.

The paper uses fMRI to decode sustained emotional states during naturalistic movie viewing and identifies specific functional connectivity patterns associated with happiness and sadness.

The paper introduces a large-scale EEG database and competition to study EEG commonality and variability across subjects, sessions, and tasks, aiming to improve machine learning algorithms for EEG-based biometrics.

This study investigates how EEG microstates are modulated by emotion dynamics and multimedia stimulation during video watching, revealing specific microstate patterns associated with different emotional states and content types.

This study evaluates ERP test-retest reliability in adolescents, finding that stronger group effects do not guarantee higher individual reliability and that latency jitter and trial variability influence this relationship.