# BCI2000: Software Resource for Adaptive Neurotechnology Research > **NIH NIH U24** · WASHINGTON UNIVERSITY · 2024 · $502,767 ## Abstract The central nervous system (CNS) changes throughout life, and its interactions with the world produce activity- dependent plasticity that enables it to acquire and maintain useful behaviors. Recent scientific and technical advances support the development of systems that create novel interactions with the CNS that can induce and guide beneficial plasticity. These systems, called adaptive neurotechnologies, measure signals from the CNS and concurrent behavior, derive from those signals the state of the CNS, and adaptively provide real-time feedback that can restore, replace, enhance, supplement or improve CNS functions impaired by injury or disease. Thus, they can provide important new therapies for neurological disorders. The development and use of adaptive neurotechnologies is an inherently multidisciplinary endeavor. The integration of knowledge and ideas from these diverse areas, and their implementation by highly sophisticated software/hardware systems, requires substantial time, effort, and multidisciplinary expertise. This slows progress in research and development of adaptive neurotechnologies and limits the number of groups that are successful in realizing these technologies. The present proposal seeks to address these two major problems. Over the past 18 years, we have developed and disseminated a software platform, called BCI2000, that supports interactions with the CNS and can implement a wide range of adaptive neurotechnologies. To date, we have provided it to more than 6,000 users worldwide who have used it to support experiments described in over 1,200 peer-reviewed publications. Despite this demonstrated value, BCI2000 adoption is limited by the requirements of substantial programming expertise and in-depth understanding of BCI2000 concepts needed to adapt and integrate BCI2000 into the specific experimental protocols and hardware technologies of a particular laboratory. Thus, most research groups cannot take advantage of the advantages provided by BCI2000. We propose to address this deficiency by simplifying the task of configuring BCI2000 for all major classes of adaptive neurotechnology experiments (Aim 1), and by providing a succinct introductory course and on-site training for scientists, engineers, and clinicians (Aim 2). We hypothesize that this work will greatly accelerate realization of adaptive neurotechnologies that will reduce the devastating impact of neurological disorders. Achieving these two aims will create and disseminate a major new software resource for adaptive neurotechnol- ogy research. Its unique utility should enable more scientists, engineers, and clinicians to engage in this exciting work. Furthermore, this common, interoperable, readily adopted, and easily adapted platform should foster a collaborative environment that enables diverse investigators to work together and complement each other. In sum, we expect that the work of this proposal will accelerate realization of novel adaptive neurotechnologies tha... ## Key facts - **NIH application ID:** 10840969 - **Project number:** 5U24NS109103-07 - **Recipient organization:** WASHINGTON UNIVERSITY - **Principal Investigator:** Peter Brunner - **Activity code:** U24 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $502,767 - **Award type:** 5 - **Project period:** 2019-05-01 → 2026-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10840969 ## Citation > US National Institutes of Health, RePORTER application 10840969, BCI2000: Software Resource for Adaptive Neurotechnology Research (5U24NS109103-07). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10840969. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*