Thomas Campbell Arnold
epilepsy. Specifically, I am interested in the application of computational methods to model
optimal stimulation parameters based on electrophysiological biomarkers and changes in
patient behavior. I am also interested in better understanding the behavioral, psychological,
and physiological drivers of seizure risk, and how these triggers can be integrated into a closed-
loop system for the treatment of epilepsy.
Georgios (George) Mentzelopoulos is a Ph.D student in the Department of Bioengineering. He completed his undergraduate studies at the University of Michigan, Ann Arbor, earning his Bachelor’s degrees in Biomedical Engineering and Electrical Engineering in April 2020.
He is interested in improving neural interfaces with both the peripheral and the central nervous systems. He is currently assisting the development of dry, super-nyquist density EEG arrays to investigate the prospect of phase-guided neuromodulation using transcranial magnetic stimulation. He is also assisting the development of EMG arrays to improve the neural interface of upper and lower limb prostheses.
In his free time, George enjoys tasting local brews, playing volleyball, and reading.
Ph.D Student, Smit Scholar
Brendan's primary focuses lie in the design, fabrication, and characterization of wearable biopotential recording devices based on the highly conductive 2D nanomaterial titanium carbide MXene (Ti3C2Tx). His published work includes an MXene-based high-density surface electromyography (HDsEMG) array for improved EMG recording compared to clinical standards. His current research explores MXene-based electrodes for electrocardiography (ECG), with a specific focus on benchmarking their performance under motion artifact tasks compared to state-of-the-art clinical electrodes. Much of Brendan's work incorporates traditional materials science and electrochemical characterization techniques, such as Raman spectroscopy, XRD, impedance spectroscopy, and cyclic voltammetry.
The unpredictability of seizure occurrence remains one of the largest sources of disease burden on persons with epilepsy. Using a rich dataset of intracranial electroencephalogram (iEEG) recordings from epilepsy patients, we seek to understand what factors may increase one’s susceptibility for seizures, and what patterns of brain signals may indicate an oncoming seizure. We use network neuroscience methods and Hidden Markov models to model seizure risk and ultimately seek to develop a warning system that gives epilepsy patients control over their seizures in normal, daily-life
My research is focused on improving electrical neurostimulation therapy for patients with epilepsy by combining modeling techniques from the field of control theory with clinical insights and intracranial device recordings. I am currently interested in finding biomarkers that can better guide neurostimulator placement and algorithms for intervention.
I am interested in the intersection of computer science and healthcare, specifically the use of machine learning to improve medical therapy and diagnostics. Currently, I am applying Natural Language Processing (NLP) methods to teach machines to read, understand, and extract clinical information from physician progress and discharge notes, with Epilepsy as an experimental lever. I plan to develop algorithms to support clinical decision-making, conduct clinical trials, and replicate critical works.
I'm interested in using novel electrode technologies to study the cellular mechanisms of seizure initiation, propagation, and termination in vivo and ex vivo. Through performing multimodal analysis, I hope to uncover new biomarkers for seizures and then optimize electrical stimulation patterns to acutely terminate seizures. I also hope to uncover the cellular identity of neurons responsible for early seizure termination in vivo.
I grew up in St. Louis and graduated from Washington University in St. Louis in 2020 with a B.A. in Physics and Chemistry. I plan to get my MD/Ph.D. in Bioengineering at Penn. My research interests include developing novel materials and technologies for neural interfaces.
I am a 2nd year ROBO MSE student pursuing my Master’s thesis at the Litt Lab. I specialize in making non-invasive wearable devices and biomedical signal processing. My focus during the thesis will be on identification of physiological biomarkers using wearable devices for the detection of different brain states and seizure activity.
My interest is to use novel materials and advanced nanofabrication technologies to develop optical, electrical, and chemical neural interfaces for basic neuroscience research and translational applications.