Title: A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces

Abstract:

Brain-computer interfaces (BCIs) often face constraints due to the low signal-to-noise ratio inherent in modalities like electroencephalography, necessitating multiple trials to reliably interpret user intent. This requirement creates a speed-accuracy trade-off, where enhanced precision typically results in slower performance. Since the optimal balance between these factors varies by application, the ability to control this trade-off is essential. Traditional metrics, such as the Information Transfer Rate, conflate speed and accuracy, which can obscure their interdependence and introduce potential biases.

To address these issues, this study introduces an evaluation framework that is independent of the classifier, paradigm, and early-stopping strategy, effectively decoupling speed from accuracy. The framework utilizes two distinct measures: Gain, which quantifies relative speed improvement, and Conservation, which measures relative accuracy preservation. These are integrated into a tunable Gain-Cons Balance, governed by the parameter $\alpha$, which regulates the speed-accuracy trade-off. By adjusting $\alpha$, users can shift the operating point without altering the underlying classifier, thereby facilitating deployment across diverse scenarios.

The proposed framework was tested on P300 event-related potential paradigms using public datasets from 63 subjects, alongside various classifiers and early-stopping strategies to achieve specific operating points in terms of speed-accuracy and bitrate. The results indicate that tuning $\alpha$ allows for the generation of fast, highly accurate, or balanced BCI behaviors, providing explicit control over the trade-off. Additionally, the method enhances the explainability of BCI behavior and supports performance prediction at the subject level. Further analysis of the Information Transfer Rate reveals a systematic bias toward speed, a phenomenon explained by the framework through the Gain and Conservation metrics. Ultimately, 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.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC