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Neuropacify: a method to transform and match a patient's intracranial EEG to their NeuroPace RNS system data
Grant Barkelew1,2, Kathleen E Kish2, Zachary T Sanger3
1McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States of America.
Journal of Neural Engineering
|December 17, 2025
Summary
This study presents a new method to convert high-resolution brain activity recordings into the format used by responsive neurostimulation (RNS) devices. This allows for better analysis of epilepsy biomarkers and personalized seizure detection parameters.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Epilepsy Research
Background:
- Responsive neurostimulation (RNS) systems monitor brain activity for epilepsy treatment.
- Technical limits in RNS devices reduce EEG recording quality.
- High-resolution intracranial EEG (iEEG) offers better data but is incompatible with RNS.
Purpose of the Study:
- To develop a technique for converting high-resolution iEEG data to RNS system format.
- To enable direct comparison between iEEG and RNS data.
- To facilitate the extraction of EEG biomarkers for RNS analysis.
Main Methods:
- Co-registered iEEG and RNS electrodes on a 3D grid.
- Applied vector math to identify corresponding electrodes.
- Derived a transfer function using spectral analysis to mimic RNS filtering and processing.
Main Results:
- Successfully converted high-resolution iEEG to RNS format.
- Validated the technique using patient data, confirming accurate transformation of EEG characteristics.
- Demonstrated the transformation of DC shifts and high-frequency oscillations.
- Provided a tutorial for adapting the method to specific device parameters.
Conclusions:
- The developed tool enables researchers to analyze high-resolution iEEG biomarkers within the RNS system's limitations.
- Facilitates the development of patient-specific seizure detection algorithms.
- Opens avenues for investigating long-term neurostimulation effects.
