Prasad R. Shirvalkar

Decoding Chronic Pain States from Distributed Intracranial Recordings

This study decodes chronic pain states from intracranial recordings and identifies network-specific neural signatures.

Predicting pain location from resting-state brain fMRI

This study uses resting-state fMRI to stratify chronic low back pain patients into phenotypes based on brain activity and sensory abnormalities, revealing distinct connectivity patterns and predicting pain locations.

Towards individualized deep brain stimulation: A stereoelectroencephalography-based workflow for neurostimulation target identification

This paper develops a statistics-driven workflow using stereoelectroencephalography to identify personalized deep brain stimulation targets through power analysis and effect size calculations.

Robust Detection of Brain Stimulation Artifacts in iEEG Using Autoencoder-Generated Signals and ResNet Classification

This paper develops a method to detect brain stimulation artifacts in iEEG using synthetic data and a ResNet classifier, achieving high accuracy on real data.

Analysis-rcs-data: Open-source toolbox for the ingestion, time-alignment, and visualization of sense and stimulation data from the Medtronic Summit RC+S system

The paper introduces an open-source Matlab toolbox for processing and analyzing data from the Medtronic Summit RC+S neurostimulation system.