A Reproducible Framework for Integrating Chronic Deep Brain Stimulation Sensing with Wearable Behavioral Monitoring

Objective: Chronic sensing-enabled deep brain stimulation (DBS) devices enable long-term neural recordings in naturalistic settings, but interpreting these data requires concurrent behavioral context. Our objective was to develop a reproducible end-to-end framework for continuous acquisition and synchronization of wearable-derived behavioral data alongside chronic DBS recordings to enable longitudinal neurobehavioral studies. Approach: We developed a publicly available software framework that includes automated wearable data ingestion, neural artifact handling, epoch-based temporal synchronization, and generation of analysis-ready neurobehavioral datasets. We tested this framework using simultaneous sensing-enabled DBS and Oura Ring recordings from three participants with obsessive-compulsive disorder. Main Results: Using this framework, we synchronized 6,384 hours of intracranial neural recordings and wearable-derived data. The resulting multimodal neurobehavioral datasets spanned months of ambulatory monitoring and integrated chronic neural recordings with sleep-wake state, physical activity, autonomic physiology, and DBS parameters. Benchtop testing revealed 12 seconds of Medtronic Percept clock drift relative to network time over a one-week period. This temporal error was substantially smaller than the 10-minute sampling interval of the chronic neural recordings, supporting reliable alignment with wearable-derived data. Significance: This work provides an open-source, reproducible framework for continuous acquisition, synchronization, and analysis of wearable-derived data alongside sensing-enabled DBS recordings. By reducing the technical barriers to generating behaviorally annotated neural datasets, this framework enables scalable longitudinal neurobehavioral studies and provides practical foundation for biomarker discovery.