Two of my PhD students and I travelled to Baltimore this week to present our latest work at the IEEE NER Conference. It was a great conference with fascinating talks and a wonderful opportunity to get back together with the field in-person again.

CSE PhD Student Ellie Strandquist presented her Weill-Neurohub funded work developing tools to collect extensive video and patient data from study participant’s homes who are enrolled in an adaptive deep brain stimulation study. This work was performed in collaboration with Simon Little of UCSF, Jack Gallant at UC Berkeley, as well as students and research staff distributed amongst the three Universities. Her work can be read in more detail here: https://ieeexplore.ieee.org/abstract/document/10123851

ECE PhD student Hanbin Cho presented her work developing software interfaces for use with the CorTec Brain Interchange and demonstrated the use of closed-loop stimulation with the platform. Her work can be read in more detail here: https://ieeexplore.ieee.org/abstract/document/10123808

I and several of my students will be attending the upcoming IEEE EMBCS Conference on Neural Engineering in Baltimore in a little over a month! We have two papers accepted, come check out our posters at the Thursday poster sessions!

Second year ECE PhD student Hanbin Cho will be presenting her ongoing work developing a gRPC-enabled microservice to enable flexible use of the CorTec Brain Interchange in upcoming clinical studies. Her paper, ID# 1570869464, is titled “Open Mind Neuromodulation Interface for the CorTec Brain Interchange (OMNI-BIC): an investigational distributed research platform for next-generation clinical neuromodulation research” and there is a preprint available here.

Third year CSE PhD student Gabrielle Strandquist will be presenting her ongoing work developing methods for video-based bradykinesia symptom assessment from remote monitoring platforms. Her paper, ID# 1570869723, is titled “In-Home Video and IMU Kinematics of Self Guided Tasks Correlate with Clinical Bradykinesia Scores” and there is a preprint available here.

Congratulations to Samantha Sun who presented her work titled “Human Intracortical Responses to Varying Electrical Stimulation Conditions Are Separable in Low-Dimensional Subspaces,” for which she was awarded the IEEE Brain Best Student Paper and was one of only four finalists for the overall IEEE SMC Best Paper!

We’re presenting two papers at the upcoming IEEE SMC 2022 conference in Prague! We presented two papers – the first detailing our experience using intracranial stimulation to deliver sensory feedback in a virtual reality environment, and the second detailing a new method for identifying time-varying neural responses to intracranial stimulation. If you’re going to SMC 2022 in Prague, come see our presentations!

Touching the Void: Intracranial Stimulation for NeuroHaptic Feedback in Virtual Reality
Courtnie Paschall, Jason Hauptman, Rajesh Rao, Jeffrey Ojemann, Jeffrey Herron
Abstract: Direct cortical stimulation of the somatosensory cortex (S1-DCS) has been shown to evoke distinct and localizable percepts, exploitable as neurohaptic feedback. In this study, we leveraged a novel virtual reality (VR) experimental platform to evaluate S1-DCS neurohaptic feedback during naturalistic object interaction. Two human subjects implanted with intracranial electrodes for seizure localization were asked to discriminate between visually identical virtual objects based on their distinct S1-DCS neurohaptic profiles. In a binary discrimination task, neurohaptic feedback was either present or absent while grasping a virtual object. In the ternary discrimination task, neurohaptic feedback was either present in one of two distinct neurohaptic sequences or absent. Both subjects performed significantly above chance in binary and ternary discrimination, demonstrating the efficacy of S1-DCS as neurohaptic feedback. Successful ternary discrimination also demonstrated that different sequences of amplitude-modulated S1-DCS at a single pair of electrodes can evoke discriminable neurohaptic percepts. Moreover, amplitude-modulated S1-DCS sequences were shown to elicit sensorimimetic percepts described as “bumpy” and “smooth” in Subject 1, and as a sensation of movement in the paralyzed hand of Subject 2. Our study demonstrates the reliability and discriminability of both simple and complex S1-DCS for neurohaptic feedback during immersive VR object interaction and supports the use of immersive VR for neurohaptic design towards the development of functional brain computer interface.

Human Intracortical Responses to Varying Electrical Stimulation Conditions Are Separable in Low-Dimensional Subspaces 
Samantha Sun, Lila Levinson, Courtnie Paschall, Jeffrey Herron, Kurt Weaver, Jason Hauptman, Andrew Ko, Jeffrey Ojemann, Rajesh Rao
Abstract: Electrical stimulation is a powerful tool for targeted neurorehabilitation, and recent work in adaptive stimulation where stimulation can be adjusted in real-time has shown promise in improving stimulation outcomes and reducing stimulation-induced side effects. Mapping the relationship between electrical stimulation input and neural activity response can help reveal interactions between stimulation and underlying neural activity and can give us tools to iterate and improve on our stimulation protocols. Here, we introduce methods for identifying low-dimensional subspaces of human intracortical responses to electrical stimulation in invasive electroencephalography. In epilepsy patients (n=4) undergoing clinical monitoring, we applied a stimulation protocol of varying stimulation amplitude and frequency in 5-second intervals to capture a range of responses to different stimulation conditions. We characterized these responses using time-frequency spectral power, applied baseline subtraction and outlier removal procedures, and performed principal component analysis across frequencies. We identified that intracortical responses to different stimulation conditions can be represented in a 3-dimensional subspace, accounting for more than 95% of the variance. Using pairwise support vector machine classification, we demonstrated separability of intracortical responses to different stimulation conditions across subjects, where this separability was contingent on performing baseline subtraction and outlier removal. Our results represent a first step towards building a mapping or predictive model from stimulation input to neural response, an important prerequisite for adaptive closed-loop stimulation for targeted neurorehabilitation.