BCI performance is often reported as offline accuracy on well-controlled datasets, but real-world evaluation requires broader metrics. In applied scenarios, task-rele- vant outcomes such as driving performance, error rates, learning gains, or user workload reduction are more informative than raw classification accuracy. Longitu­dinal and field studies are needed to assess robustness over days or weeks, as well as user acceptance and trust. For pBCIs, cross-task application is particularly critical: decoders trained in one paradigm are often applied to different tasks or environ­ments, where signal characteristics and label structure change. Similarly, applying event-based classifiers (e.g., error reaction decoders) continuously to live-streamed data is nontrivial, as the live data may not have event markers at the same temporal accuracy level as the training data, or no markers at all (Pan et al., framewo rates, and the impact of neuroadaptive inte rventions on actual user behavior.