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.

翻译：随着脑机接口系统从辅助技术向更多样化的应用场景过渡，其速度、可靠性和用户体验变得日益重要。动态停止方法通过实时决定是输出结果还是等待更多信息，从而提升脑机接口系统的速度。该方法利用试验方差，使得优质试验能够被更早地检测出来，从而在不显著降低准确率的前提下加速处理过程。现有的动态停止算法通常优化诸如每分钟符号数（SPM）和信息传输率（ITR）等指标。然而，这些指标可能无法准确反映特定应用或用户类型的系统性能。此外，许多方法依赖于需要大量训练数据的任意阈值或参数。我们提出一种基于模型的方法，该方法利用我们对底层分类模型已有的分析知识。通过采用风险最小化策略，我们的模型能够精确控制错误类型以及精度与速度之间的平衡。这种适应性使其非常适合定制脑机接口系统，以满足各种应用场景的多样化需求。我们在公开数据集上验证了所提出的方法，并将其与已有的静态和动态停止方法进行比较。结果表明，我们的方法提供了广泛的精度-速度权衡范围，并且比基线停止方法实现了更高的精度。