# Decoding / encoding somatosensation from the hand area of the human primary somatosensory (S1) cortex for a closed-loop motor / sensory brain-machine interface (BMI)

> **NIH NIH K23** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $192,151

## Abstract

PROJECT SUMMARY/ABSTRACT
Upper limb reaching and grasping movements require complex cortical control circuits involving both motor-
control outputs and real-time somatosensory feedback. Neurological disorders such as strokes, brain
trauma, and spinal cord injury may result in a loss of the ability to perform these tasks. Many teams, including
our own, are working to restore upper extremity function by using human neural signals to control the
movements of a robotic limb with multiple degrees of freedom [1-3]. However, without somatosensory
feedback, even the most basic limb movements are difficult to perform in a fluid and natural manner [4, 5].
There have only been a limited number of human studies exploring how to generate somatosensory feedback.
Using subdural electrocorticography (ECoG) grids placed on the human primary somatosensory (S1) hand area
in patients with epilepsy who require intracranial monitoring, we propose studies directed toward understanding
how somatosensation is cortically encoded and how we can restore upper extremity somatosensation via
electrical stimulation. To accomplish this, I have assembled a multidisciplinary mentoring team, led by Dr.
Gianluca Lazzi, with an established history of success in mentoring early investigators. From my mentoring
team, I plan on learning about neural modeling, study design and biostatistics, and medical device
development. My long-term goal is to become an independent NIH-funded neurosurgeon-scientist who makes
significant contributions to our understanding of sensorimotor integration. In Aim 1 we will use the participants
own ECoG responses to real touch to guide a systematic mapping of stimulation parameter space to find
distinct percepts of somatosensation. Much like how clinical neurostimulators such as deep brain stimulators
(DBS) for movement disorders and vagus nerve stimulators (VNS) are therapeutic only at specific stimulation
settings, we hypothesize that we will find specific stimulation combinations that result in different types of
somatosensation. In Aim 2 we will compare task performance using artificial somatosensation versus native
touch. In Aim 3 we will quantify how real touch and artificial somatosensation generated by ECoG stimulation
differ in response time between real touch/stimulation and participant perception. These results and the
mentoring provided through this K23 program will be a critical foundation for my transition to an independent
investigator in sensorimotor integration.

## Key facts

- **NIH application ID:** 10055151
- **Project number:** 1K23NS114190-01A1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Brian Lee
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,151
- **Award type:** 1
- **Project period:** 2020-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055151

## Citation

> US National Institutes of Health, RePORTER application 10055151, Decoding / encoding somatosensation from the hand area of the human primary somatosensory (S1) cortex for a closed-loop motor / sensory brain-machine interface (BMI) (1K23NS114190-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10055151. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*