# Enhancing an open-source brain-computer interface software for greater adoption and physiologic data sharing

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $230,994

## Abstract

SUMMARY
There are high technological and software demands associated with conducting brain-computer interface (BCI)
research. BCIs are computer-facilitated systems that rely on direct, real-time measures of brain activity for
environmental interaction. In order to accelerate the development and accessibility of BCIs, the OHSU BCI
research team, during the parent award (R01DC009834), created an open-source software platform, BciPy,
available on GitHub. BciPy is written in Python in order to reduce many barriers in the field related to data
sharing, storage, and testing new algorithms with existing datasets. BciPy already engages over 30 community
members with 17,500 downloads. This supplement allows us to increase the user community through better
engagement tooling and integration with cloud services to ensure that experimental data are more accessible
and faster to obtain by the broader community than is currently available. This administrative supplement
proposes to (1) enhance the use of established open-source BciPy software and (2) accelerate the
management, storage, and sharing, via cloud services, of electroencephalography (EEG) and other
physiological data. A unique scientific contribution for data science is our dataset, physiologic data acquired
from people with severe speech and physical impairments (SSPI) for use in BCI research.
Two specific aims are proposed that achieve the quality recognized by the FAIR guiding principles for scientific
data management and stewardship. Specific Aim 1 will improve data sharing and enhance software
dissemination and collaboration. We will extend BciPy to support a flexible and extensible architecture for
specifying and encoding task-, session-, user-, and experiment-level metadata, so that investigators can easily
describe their own tasks and protocols for effective data dissemination. We will refine BciPy to accomplish
experiment-level management and tagging in line with proposed data standardization methods, such as the
Brain Imaging Data Structure (BIDS). We will launch a website at bcipy.github.io to communicate about our
listservs, Slack channels, and future workshops. Published guidelines will initiate data sharing protocols by
setting up storage resources for automatic upload using Amazon Web Services. Specific Aim 2 will facilitate
cloud integration of BciPy data. Offline evaluation of user-generated research algorithms will be encouraged
with technical assistance available. Proposed sharing and functionality efforts will significantly increase
adoption and implementation of BciPy software and our exceptional acquired physiological data within the
international BCI research and development community. Data storage mechanisms are proposed that remain
affordable and sustainable in order to support long-term maintenance. The OHSU BCI research team is
uniquely positioned for this open data science supplement. With capacity and proven expertise, these aims will
significantly move the field forward...

## Key facts

- **NIH application ID:** 10165379
- **Project number:** 3R01DC009834-11A1S1
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** MELANIE FRIED-OKEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $230,994
- **Award type:** 3
- **Project period:** 2009-02-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10165379

## Citation

> US National Institutes of Health, RePORTER application 10165379, Enhancing an open-source brain-computer interface software for greater adoption and physiologic data sharing (3R01DC009834-11A1S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10165379. Licensed CC0.

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