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The Role of MER Processing Pipelines for STN Functional Identification During DBS Surgery: A Feature-Based Machine

Vincenzo Levi^1,2, Stefania Coelli², Chiara Gorlini¹

¹Functional Neurosurgery Unit, Neurosurgery Department, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Giovanni Celoria 11, 20133 Milan, Italy.

Bioengineering (Basel, Switzerland)

|December 30, 2025

Summary

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This summary is machine-generated.

Optimal preprocessing of microelectrode recording (MER) data improves subthalamic nucleus (STN) identification for deep brain stimulation (DBS) surgery in Parkinson's Disease (PD). Artifact rejection and outlier management enhance machine learning accuracy, while feature normalization can decrease performance.

Area of Science:

Neurosurgery
Biomedical Engineering
Computational Neuroscience

Background:

Microelectrode recording (MER) is crucial for validating subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) for Parkinson's Disease (PD).
Machine learning (ML) models are increasingly used to enhance STN localization from MER data.
The influence of preprocessing steps on ML classifier performance for MER data remains underexplored.

Purpose of the Study:

To systematically evaluate the impact of various preprocessing pipelines on the accuracy of ML-based STN localization using MER data.
To identify optimal artifact removal, outlier handling, and feature normalization strategies for MER data preprocessing.
To assess the interpretability of ML models using SHAP analysis to understand feature importance.

Main Methods:

Keywords:

deep brain stimulation (DBS)machine learning (ML)microelectrode recordings (MER)subthalamic nucleus (STN)

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Investigated 24 distinct preprocessing pipelines combining artifact removal (COV, BCK), outlier handling (ORH), and feature normalization techniques.
Evaluated pipeline performance using three ML models (Random Forest, Support Vector Machine, Logistic Regression).
Utilized SHAP (SHapley Additive exPlanations) analysis to determine feature importance across different preprocessing configurations.

Main Results:

Artifact rejection (COV, BCK) and optimized outlier management (ORH) consistently improved classification performance.
Hemisphere-specific feature normalization prior to classification led to performance degradation across all evaluated metrics.
The Random Forest model, applied to data preprocessed with COV artifact rejection and ORH outlier management, achieved the highest accuracy (0.945).

Conclusions:

Effective artifact rejection and outlier treatment are critical for accurate MER-based STN identification in DBS surgery.
Preliminary feature normalization strategies can potentially impair ML model performance in this context.
SHAP-based interpretability provides valuable insights for refining ML pipelines and developing robust protocols for MER-guided DBS targeting.