N2G calibrator: a cross-subject domain adversarial training framework for gait tracking from neural signals in Parkinson’s disease

Abstract Adaptive deep brain stimulation has enabled machine learning models to track motor states from neural signals with improved accuracy, aiming to provide electrical stimulation accordingly. Such data-driven techniques necessitate extensive user-specific data collection involving repetitive tasks and additional sensors to quantify continuous movements, due to variations in neural signals between individuals. In this study, we introduce Neural-to-Gait Calibrator, a cross-subject deep learning framework that leverages collective neural data to track gait performance of users with Parkinson’s disease. Our frame-work utilizes domain adversarial learning to calibrate target user’s neural signals using data from other individuals, removing the need for synchronous gait recording systems thereby enabling personalized model calibration outside equipped clinical settings. The framework’s effectiveness was demonstrated through a significant reduction in error rates compared to models trained with data from other individuals without calibration, achieving performance comparable to that of models trained directly with labeled target data. Competing Interest Statement The authors have declared no competing interest. Clinical Trial NCT02384421 NCT01990313 Funding Statement This work was supported in part by the following: NINDS UH3NS107709, NINDS UG3NS128150, NINDS UH3NS128150, NINDS R21 NS096398-02, Michael J. Fox Foundation (9605), Robert and Ruth Halperin Foundation, John A. Blume Foundation, John E Cahill Family Foundation, and Medtronic PLC who provided the devices used in this study but no additional financial support. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Food and Drug Administration with an Investigational Device Exemption (G130186) and by the Stanford University Institutional Review Board (25916 and 30880). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes