Our research addresses the interdisciplinary intersection of brain science and neural engineering. Taking neurophysiological signals — EEG/ERP, magnetoencephalography (MEG), and stereoelectroencephalography (SEEG) — as the primary objects, and modern signal processing, machine learning, and complex network analysis as the core methodologies, we conduct cross-disciplinary research for the assessment and non-invasive intervention of neurological disorders, with applications in sleep health, cognitive assessment, disorders of consciousness, epilepsy, and stroke rehabilitation. The main research fields include:

Field 1: Biomedical Signal Processing and Neural DecodingThis field focuses on feature extraction, fusion, and decoding of EEG signals: extracting and fusing neural features such as event-related potentials (ERPs) and phase–amplitude coupling (PAC); characterizing domain shift and developing adaptive generalization methods for decoding across subjects and devices; applying auditory evoked potential analysis to the assessment and paradigm optimization of consciousness states in patients with disorders of consciousness (DOC); and developing adaptive discrimination methods for motor-imagery brain–computer interfaces (BCIs) for rehabilitation training and human–machine interaction. Representative outputs: Biomedical Signal Processing and Control (2027); Frontiers in Neurology (2026); Brain Sciences (2025, 2026); CME 2025; ICBIP 2025

Field 2: Brain Network ComputationThis field applies complex network and graph-theoretic methods to neuroimaging and neurophysiological data to characterize network structure and dynamics across brain states: characterizing subnetwork developmental patterns in children with ASD via non-negative matrix factorization (NMF); investigating the relationship between brain network structure and cognitive performance in children using MEG; analyzing epileptogenic network dynamics in drug-resistant epilepsy using SEEG; constructing brain functional networks from task-related EEG activation; and assessing brain functional status in stroke patients based on EEG functional network characteristics. Representative outputs: PLoS One (2017); Computers in Biology and Medicine (2023); NeuroRehabilitation (2022); patents

Field 3: Non-invasive Neuromodulation and Clinical TranslationThis field focuses on the clinical translation of neural signals: conducting prospective studies on the therapeutic efficacy of transcutaneous vagus nerve stimulation (tVNS) for epileptic encephalopathy and other conditions; and exploring clinical applications such as intraoperative monitoring based on evoked potentials and BCI-based rehabilitation training, advancing the deployment of non-invasive neuromodulation and neural signal analysis in rehabilitation medicine and neurological care. Representative outputs: Journal of Child Neurology (2026); patents