Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials to reliably decode user intentions. This induces a speed-accuracy trade-off, whereby higher accuracy comes at the cost of speed. The speed-accuracy balance is application-dependent, motivating controllable trade-offs. Conventional metrics, such as the Information Transfer Rate, combine speed and accuracy obscuring their dependence and potentially introducing biases. In this study, we propose an evaluation framework independent of classifier, paradigm, and early-stopping strategy that separates speed and accuracy. We employ two measures, Gain (relative speed improvement) and Conservation (relative accuracy preservation), and combine them into a tunable Gain-Cons Balance controlled by α, regulating the speed-accuracy trade-off. The parameter adjusts the operating point without modifying the classifier, facilitating deployment across scenarios. The framework was evaluated on P300 event-related potential paradigms using public recordings from 63 subjects as well as multiple classifiers and early-stopping strategies to achieve distinct operating points in speed-accuracy and bitrate. Results show that tuning α yields fast, accurate, or balanced BCI behaviours, demonstrating explicit control of the speed-accuracy trade-off. The method supports subject-level performance prediction and improves explainability of BCI behaviour. Further analysis of the Information Transfer Rate reveals a systematic bias toward speed, explained by the proposed framework through the Gain and Conservation measurements. Overall, this work establishes the speed-accuracy trade-off as a controllable design variable validated on public P300-based paradigms, enabling transparent evaluation and application-specific optimization of BCIs.

翻译：脑机接口（BCI）受限于头皮脑电图（EEG）等模态的低信噪比，需通过多次试验来可靠解码用户意图。这导致了速度-准确率权衡：更高的准确率以速度为代价。速度与准确率的平衡取决于具体应用场景，因此需要可控的权衡机制。传统指标（如信息传输速率）将速度与准确率融合在一起，既模糊了二者的依赖关系，也可能引入偏差。本研究提出一种独立于分类器、实验范式和提前停止策略的评估框架，将速度与准确率分离。我们采用两个度量指标——增益（相对速度提升）与保持度（相对准确率保持），并通过参数α调控其权衡关系，构建可调节的速度-准确率平衡（Gain-Cons Balance）。该参数无需修改分类器即可调整工作点，便于跨场景部署。基于P300事件相关电位范式，利用63名受试者的公开记录、多种分类器及提前停止策略，在速度-准确率和比特率维度上获得不同工作点以评估该框架。结果表明，调节α可产生快速型、准确型或均衡型BCI行为，实现对速度-准确率权衡的显式控制。该方法支持个体水平性能预测，并提升BCI行为的可解释性。对信息传输速率的进一步分析揭示了其固有的偏向速度的系统性偏差，该偏差可通过本框架的增益与保持度测量加以解释。总体而言，本研究将速度-准确率权衡确立为可控设计变量，基于公开的P300范式验证其有效性，为BCI的透明评估与场景特异性优化提供了支持。