NeoDBS: Open-Source Platform for Visualization and Analysis of Electrophysiological Recordings from Deep Brain Stimulation Systems

Abstract

Optimization of deep brain stimulation (DBS) therapy for neurological and neuropsychiatric disorders depends on objective quantitative biomarkers that can guide stimulation parameter adjustments. With the recent introduction of new-generation DBS systems capable of simultaneously stimulating brain activity and recording local field potentials (LFP), there is increasing demand for platforms that enable efficient visualization and analysis of these signals for electrophysiological biomarkers identification. To address the limitations of currently available toolboxes that require advanced signal processing skills and rely on proprietary software, we present NeoDBS, an open-source Python platform designed for ingestion and advance signal visualization and processing of LFP signals from DBS systems through an easy-to-use graphical interface. NeoDBS is a user-centered platform that offers predefined analysis pipelines with the aim of facilitating electrophysiological biomarker investigation for DBS across different brain disorders. Custom analysis pipelines are also available for users to leverage the signal analysis tools to their research needs. Critical functionalities for longitudinal biomarker research are featured in NeoDBS, such as batch file processing and event-locked analysis for in-clinic and at-home recordings. This combination of accessibility, user-experience and advanced signal processing tools makes NeoDBS an environment that propels easy and fast electrophysiological biomarker research for DBS, across patients, sessions, and stimulation parameters.