A methodological framework for explicit control of the speed–accuracy trade-off in P300-based brain–computer interfaces

Current brain–computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, requiring multiple trials to reliably decode user intentions. This induces an inherent speed–accuracy trade-off, whereby higher accuracy comes at the cost of speed. Since the acceptable speed–accuracy balance depends on the application, explicit user-controllable trade-offs are needed. Conventional metrics such as the Information Transfer Rate (ITR) combine speed and accuracy, obscuring their individual contributions and systematic biases. We propose an evaluation framework conceptually independent of the classifier, paradigm, and early-stopping strategy, explicitly representing and optimizing speed and accuracy. Although conceptually paradigm-independent, validation is limited to P300-based paradigms. We combine two measures from prior work, Gain (relative speed improvement) and Conservation (relative accuracy preservation), into a tunable balance controlled by α to regulate the speed–accuracy trade-off. α adjusts the operating point without modifying the underlying classifier, enabling application-specific configuration. We evaluated the framework on public recordings from 63 subjects, multiple classifiers, and early-stopping strategies to achieve distinct speed–accuracy operating points. Results show that tuning α enables fast, accurate, or balanced BCI behaviours, demonstrating explicit speed–accuracy control. The method characterizes population-level performance distributions and improves explainability. Further analysis of the ITR, interpreted through Gain and Conservation , suggests a tendency toward faster decision regimes. Overall, this work establishes the speed–accuracy trade-off as a controllable design variable, validated on public P300 paradigms, enabling transparent and application-specific BCI optimization.