Local Field Potential Recordings Using Deep Brain Stimulation: A Practical Workflow and Open-Source Signal Processing Pipeline

Clinical Snapshot

Key Findings

• P Value: Not reported — hypothesis testing not performed

• Effect Size: Not applicable — no comparative effect size reported; this is a methodological demonstration study

• Primary Outcome: Successful demonstration of BrainWave-DBS pipeline functionality for LFP data import, preprocessing, spectral analysis, and visualisation using a representative Parkinson's disease dataset, with identification of key technical insights for LFP acquisition

• Nnt Or Sensitivity: Not applicable for this study type; relevant future metrics would include sensitivity/specificity of pipeline-derived beta-band biomarkers for detecting DBS-responsive states in adaptive neuromodulation paradigms

• Confidence Interval: Not reported — no inferential statistics presented

Clinical Application

Feasibility for clinical research adoption is moderate-to-high given the open-source availability of BrainWave-DBS. However, practical implementation requires: (1) DBS hardware with LFP streaming capability (not all implanted devices support this); (2) institutional data governance frameworks for handling patient-derived neural data; (3) staff with sufficient neurophysiology and computational literacy to operate and interpret the pipeline; (4) integration with existing clinical workflows without disrupting standard DBS programming appointments. In Australia, DBS for Parkinson's disease is performed at major neurosurgical centres including Royal Melbourne Hospital, Westmead Hospital, Princess Alexandra Hospital, and others. The Medtronic Percept PC — the most widely used sensing-enabled DBS device in Australia — is TGA-approved and supports LFP streaming, making BrainWave-DBS potentially directly applicable. The RACGP and Movement Disorder Society of Australia do not currently have specific guidelines on LFP-guided DBS programming, reflecting the early-stage nature of this technology in routine clinical practice. PBS listing does not cover LFP analysis software, but open-source tools remove this barrier. Australian DBS research groups, including those affiliated with the Bionics Institute and university-based neuroscience centres, may find this pipeline valuable for translational research. Data sovereignty and privacy considerations under the Australian Privacy Act 1988 and state-based health information legislation would need to be addressed before routine clinical implementation. Patients with Parkinson's disease (and potentially other movement disorders, epilepsy, or psychiatric conditions) who have received DBS implants with chronic LFP recording capability. Most immediately applicable to neurophysiology researchers and clinician-scientists at DBS centres seeking to implement structured LFP analysis workflows.

Abstract

Deep brain stimulation (DBS) systems with electrophysiological recording capabilities offer a unique opportunity to chronically record local field potentials (LFP) from implanted electrodes, enabling real-time monitoring of subcortical neural dynamics and the development of adaptive neuromodulation strategies. However, translating raw LFP into clinically relevant biomarkers remains challenging due to numerous technical considerations. To address this, we share both a practical guide for conducting LFP experiments and BrainWave-DBS, an open-source pipeline that implements the workflow and supports analysis of patient-derived LFP data. The pipeline covers data import, preprocessing, spectral analysis, and visualization, offering a reproducible framework for LFP analysis. Furthermore, we demonstrate the functionality of the pipeline using a representative Parkinson's disease dataset and highlight key technical insights gained through this work. By improving access to neural signal acquisition and analysis, our goal is to foster broader adoption of this technology, which could advance closed-loop neuromodulation of deep brain structures.

References

1. 1.Guillemette, A., Gobeil, M.-A., Dominguez-Vargas, A.-U., Silhadi, M., Bergeron, D., Vogt, R., Moradi, N., Niazi, F., Weil, A. G., Hadjinicolaou, A., Obaid, S., Assi, E. B., Jodoin, N., Iorio-Morin, C., Dancause, N., & Fournier-Gosselin, M.-P. (2026). Local field potential recordings using deep brain stimulation: A practical workflow and open-source signal processing pipeline. Brain Topography. https://doi.org/10.1002/mds.30076