# RAVE: A New Open Software Tool for Analysis and Visualization of Electrocorticography Data

> **NIH NIH R24** · BAYLOR COLLEGE OF MEDICINE · 2020 · $236,620

## Abstract

Project Summary/Abstract
 A fast-growing technique in human neuroscience is electrocorticography (ECOG), the only technique
that allows the activity of small population of neurons in the human brain to be directly recorded. We use the
term ECOG to refer to the entire range of invasive recording techniques (from subdural strips and grids to
penetrating electrodes) that share the common attribute of recording neural activity from the human brain
with high spatial and temporal resolution. While this ability has resulted in many high-impact advances in
understanding fundamental mechanisms of brain function in health and disease, it generates staggering
amounts of data as a single patient can be implanted with hundreds of electrodes, each sampled thousands of
times a second for hours or even days. The difficulty of exploring these vast datasets is the rate-limiting step in
using them to improve human health. We propose to overcome this obstacle by creating an easy-to-use,
powerful platform designed from the ground up for the unique properties of ECOG. We dub this software tool
RAVE (“R Analysis and Visualization of Electrocorticography data”).
 The first goal of Aim 1 is to release RAVE 1.0 to the entire ECOG community by month 6 of the first
funding period. This will maximize transformative impact by putting the new tools in the hands of users as
quickly as possible, facilitating rapid adoption. The design philosophy of RAVE is driven by three imperatives.
The first is to keep users "close to the data" so that users may make discoveries about the brain without being
misled by artifacts. The second imperative is rigorous statistical methodology. The final imperative is "play well
with others". As described in Aim 2, our approach will make it easy to seamlessly incorporate new and existing
analysis tools written in Matlab, C++, Python or R into RAVE, giving users the best of both worlds: advanced
but easy-to-use visualization of results from ECOG experiments, whether they are analyzed with the off-the-
shelf tools routines provided with RAVE or novel tools developed by others.

## Key facts

- **NIH application ID:** 9933092
- **Project number:** 5R24MH117529-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Sameer Anil Sheth
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $236,620
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9933092

## Citation

> US National Institutes of Health, RePORTER application 9933092, RAVE: A New Open Software Tool for Analysis and Visualization of Electrocorticography Data (5R24MH117529-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9933092. Licensed CC0.

---

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