A Bayesian dynamic stopping method for evoked response brain-computer interfacing

TL;DR

This paper introduces a Bayesian dynamic stopping method for brain-computer interfaces that optimizes speed and accuracy by leveraging model knowledge and risk minimization, adaptable to diverse application needs.

Contribution

A novel model-based dynamic stopping approach that uses risk minimization for precise control over error types and speed, improving BCI performance over existing methods.

Findings

Achieves higher precision than baseline methods.

Provides a broad range of accuracy-speed trade-offs.

Validated on a public dataset with superior results.

Abstract

As brain-computer interfacing (BCI) systems transition from assistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data. We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimisation approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications. We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.