# Closed-Loop Stimulus Optimization to Increase Communication Efficiency in Brain-Computer Interfaces

> **NIH NIH R21** · DUKE UNIVERSITY · 2021 · $266,638

## Abstract

ABSTRACT
This administrative supplement is in response to the Notice of Special Interest to improve the artificial
intelligence and machine learning (AI/ML)-Readiness of NIH-supported sata (NOT-OD-21-094). Summary of
Parent Award. Augmentative and alternative communication (AAC) systems are used by people with
communication and motor disabilities, such as amyotrophic lateral sclerosis (ALS), to communicate and
interact with their environment. There are conventional AAC devices that are controlled by access methods
such as touch, switch, head tracking and eye gaze; however, these access methods become difficult or
impossible to use when sustained muscle control or voluntary motor control is lost. There are brain-computer
interface (BCI) communication systems, such as the P300 speller, that use sensory stimulation to elicit and
then detect sensory neural responses in electroencephalography (EEG) data. However, communication with
stimulus driven BCIs is suboptimal due to relying on inherently noisy EEG data and highly variable neural
responses for BCI control. BCI communication rates can potentially be improved by leveraging information in
EEG data in real-time to optimally tune the BCI system’s parameters to maximise BCI performance under
conditions of uncertainty. This work investigates a novel closed-loop stimulus selection algorithm that optimises
the stimulus presentation schedule of the P300 speller in real-time based on the measured EEG data and the
BCI system’s belief about the user’s intent. Aim 1 develops and tests the novel algorithm in a cohort of abled-
bodied individuals to evaluate the real-time feasibility and utility of closed-loop stimulus selection. Aim 2 will
test the closed-loop stimulus selection algorithm in a cohort of individuals with ALS to assess the performance
of the algorithm in target BCI end users. Goals of this Supplement. There is a current unmet need for large,
diverse BCI datasets that include target BCI end users for BCI algorithm development, particularly with the
popularity of data hungry deep learning models. Based on NIH-supported research for 10+ years, we have
acquired a large amount of single- and multi-session data from P300 speller studies with abled-bodied
participants and participants with ALS using different stimulus presentation paradigms. Guided by FAIR
principles, in this supplement: (1) we will perform data curation, data cleaning and data engineering to develop
a cross-platform readable P300 speller dataset with common data and metadata elements and make this
dataset publicly available; and we will demonstrate the usability of this dataset in (2) an AI/ML application
focused on developing robust data representations to mitigate the negative effect of variabilities in EEG data
on AI/ML algorithms; and (3) in student research programs focused on skill development in data science and
AI/ML. A large, inclusive and accessible BCI dataset will have significant impact in the BCI community and the
broade...

## Key facts

- **NIH application ID:** 10412578
- **Project number:** 3R21DC018347-02S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Boyla Mainsah
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $266,638
- **Award type:** 3
- **Project period:** 2020-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412578

## Citation

> US National Institutes of Health, RePORTER application 10412578, Closed-Loop Stimulus Optimization to Increase Communication Efficiency in Brain-Computer Interfaces (3R21DC018347-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10412578. Licensed CC0.

---

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