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

> **NIH NIH R21** · DUKE UNIVERSITY · 2021 · $146,883

## Abstract

Abstract
Assistive devices such as augmentative and alternative communication (AAC) systems are used by people with
communication and motor disabilities, such as amyotrophic lateral sclerosis (ALS, commonly known as Lou
Gehrig’s disease), to communicate and interact with their environment. There are various commercially
available 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 is more
challenging 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, and these communication aids do not require any motor
control on the part of the affected individual. However, the accuracies and spelling speeds of stimulus-driven
BCIs are suboptimal due to the inherent limitations associated with relying on sensory stimulation, which
generates highly variable neural responses, as well as the necessity of processing inherently noisy EEG data to
extract the relevant neural information that is needed to control the BCI. Current BCI communication rates can
potentially be improved with closed-loop optimisation techniques that exploit information from the user’s
responses to previous stimuli to optimally tune the BCI system’s parameters to achieve the desired goal of
maximising system performance under conditions of uncertainty. A closed-loop strategy can be used to select
stimuli that are maximally informative of the user’s intent given the neural responses that are being measured,
and I hypothesise that this data-adaptive approach to stimulus selection will minimise BCI decision errors and
achieve better device control. Conventional BCIs use open-loop stimulus control methods as the stimulus
presentation schedule is typically set in advance or occurs randomly, and there has been limited development
of closed-loop stimulus paradigms in BCIs. The goal of the research that I propose is to investigate the
feasibility of a novel closed-loop stimulus selection algorithm that will optimise the BCI stimulus presentation
schedule in real-time based on the measured EEG data and the BCI system’s belief about the user’s intent, with
proof-of-concept demonstrated in the P300 BCI speller. Specific Aim 1 will initially develop and test the novel
algorithm in a non-disabled cohort to leverage the time efficiency and practicality of non-disabled participant
studies to evaluate the real-time feasibility and potential utility of the closed-loop stimulus selection algorithm.
Specific Aim 2 will test the closed-loop stimulus selection algorithm in individuals with ALS to assess the
performance of the algorithm in a clinically relevant cohort. The successful development and testing of the
proposed closed-loop stimulus selection algorithm in a challenging s...

## Key facts

- **NIH application ID:** 10074558
- **Project number:** 5R21DC018347-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Boyla Mainsah
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $146,883
- **Award type:** 5
- **Project period:** 2020-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10074558

## Citation

> US National Institutes of Health, RePORTER application 10074558, Closed-Loop Stimulus Optimization to Increase Communication Efficiency in Brain-Computer Interfaces (5R21DC018347-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10074558. Licensed CC0.

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