# Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints

arXiv: https://arxiv.org/abs/2109.06011 · HTML: https://mlsanity.com/paper/2109.06011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots](https://mlsanity.com/paper/1805.02732/) (2018)
- [Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization](https://mlsanity.com/paper/1605.01270/) (2016)

## Built on by (most cited first, 2 of 2)

- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)

## Abstract (arXiv)

The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature. The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent. Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs. We evaluate a standalone random search and a combined Bayesian optimization with random search in a closed-loop auditory event-related potential protocol. We aimed at finding the individually best stimulation speed -- also known as stimulus onset asynchrony (SOA) -- that maximizes the classification performance of a regularized linear discriminant analysis. To make the Bayesian optimization feasible under noise and the time pressure posed by an online BCI experiment, we first used offline simulations to initialize and constrain the internal optimization model. Then we evaluated our approach online with 13 healthy subjects. We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values. Our data suggests, however, that subjects were influenced to very different degrees by the SOA parameter. This makes the automatic parameter selection infeasible for subjects where the influence is limited. Our work proposes an approach to exploit the benefits of individualized experimental protocols and evaluated it in an auditory BCI. When applied to other experimental parameters our approach could enhance the usability of BCI for different target groups -- specifically if an individual disease progress may prevent the use of standard parameters.