Identifying Neural Tipping Points of Freezing of Gait in Parkinson’s Disease: An EEG-Based Early Warning and Closed-Loop Stimulation Study

Objective: To investigate whether scalp electroencephalographic (EEG) early-warning markers can predict freezing of gait (FoG) in Parkinson’s disease (PD) and to evaluate the therapeutic effects of a non-invasive closed-loop neuromodulation approach. Methods: We retrospectively reviewed 100 PD patients with clinically confirmed FoG. Patients with prior deep brain stimulation (DBS) were excluded. Based on treatment records, patients were allocated to a closed-loop group (n=50) receiving EEG-informed transcranial alternating current stimulation (tACS) or a control group (n=50) receiving optimized medical therapy and physiotherapy. EEG variance, lag-1 autocorrelation, beta-band synchronization, and network entropy were extracted as early-warning signals. Outcomes included FoG frequency and duration, gait velocity, stride variability, UPDRS-III gait item, and FoG-Q, assessed at baseline, 2, 4, 8 weeks, and 3 months. Results: Compared with controls, patients receiving closed-loop tACS showed significant improvements from Week 4 onward, including fewer and shorter FoG episodes, faster gait, and reduced stride variability (all p < 0.001 at 3 months). Clinical gains were paralleled by EEG changes, with reductions in variance, lag-1 autocorrelation, and beta synchrony, and increases in network entropy, all correlating with FoG improvement. Conclusion: Non-invasive closed-loop tACS guided by EEG early-warning markers significantly alleviates FoG and improves gait stability in PD patients. These findings highlight variance, lag-1 autocorrelation, and beta synchrony as practical biomarkers for personalized neuromodulation. Longer-term studies are needed to establish durability and safety.