  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.
