arXiv subjects

Jeffrey Herron

Publications and source records attributed to Jeffrey Herron.

3 recordsLinked to original sources

arXiv2025

PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals

This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, leveraging Power-In-Band (PIB) and signal coherence as distinguishing features. Our method evaluates the effectiveness of traditional machine learning algorithms on a manually curated electrophysiological dataset obtained from intracranial electroencephalography (iEEG), offering valuable insights into model performance for pain detection. Furthermore, we identify critical electrode pairings associated with acute pain, providing a clearer understanding of the neural markers that differentiate pain states. This work highlights the potential of targeted feature engineering in advancing pain classification, setting the stage for future enhancements in real-time and personalized pain assessment tools. Additionally, these findings have promising applications in neuromodulation and Deep Brain Stimulation (DBS), where adaptive and closed-loop systems could leverage identified pain markers to modulate pain-related brain regions more precisely, offering improved therapeutic options for chronic pain management

eess.SP↗

arXiv2016

A Metric for Evaluating and Comparing Closed-Loop Deep Brain Stimulation Algorithms

Objective: Closed-loop deep brain stimulation (DBS) may improve current clinical DBS treatment for neurological movement disorders, but control algorithms may perform differently across patients. New metrics are needed for comparing and evaluating closed-loop algorithm performance that address the specific needs of closed-loop neuromodulation controllers. Approach: A metric is proposed for system performance that includes normalized terms that can be used to compare algorithm performance for a patient. This metric was evaluated using two closed-loop control algorithms that were tested in patients with Parkinson's Disease (PD) who experience rest tremor. Main Results: The metric's resulting balance between tremor treatment and power savings varied on a per patient and algorithm basis. This was expected given how each trial resulted in a variable reduction in stimulation power at the cost of additional tremor for the patient when compared to open-loop stimulation. Significance: The proposed metric will aid in clinical evaluation of new algorithms and provide a benchmark for future system designers. This will be important given the growing potential applications of dynamically adjusted neural stimulation. ClinicalTrials.gov Identifier: NCT02384421.

q-bio.NC↗

arXiv2015

To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots

Teleoperated robots are playing an increasingly important role in military actions and medical services. In the future, remotely operated surgical robots will likely be used in more scenarios such as battlefields and emergency response. But rapidly growing applications of teleoperated surgery raise the question; what if the computer systems for these robots are attacked, taken over and even turned into weapons? Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II, an advanced teleoperated robotic surgery system. We identify a slew of possible cyber security threats, and experimentally evaluate their scopes and impacts. We demonstrate the ability to maliciously control a wide range of robots functions, and even to completely ignore or override command inputs from the surgeon. We further find that it is possible to abuse the robot's existing emergency stop (E-stop) mechanism to execute efficient (single packet) attacks. We then consider steps to mitigate these identified attacks, and experimentally evaluate the feasibility of applying the existing security solutions against these threats. The broader goal of our paper, however, is to raise awareness and increase understanding of these emerging threats. We anticipate that the majority of attacks against telerobotic surgery will also be relevant to other teleoperated robotic and co-robotic systems.

cs.RO↗