Brain-computer interfaces (BCI) enable new forms of communication by decoding brain signals, with code-modulated visual evoked potentials (c-VEP) showing promise in accuracy, calibration, and selection times. However, challenges such as reducing visual fatigue and developing user-friendly wearables remain. This study evaluates c-VEP speller performance in realistic scenarios, a crucial step for integrating BCI into practical, everyday use. Two key aspects are explored: stimulus opacity for background blending and background effects. Ten healthy participants tested six conditions with varying opacity and backgrounds. The study found c-VEP performance remained robust across different backgrounds, with no recalibration needed when scenarios changed. However, performance varied with opacity settings. When stimulus contrast shifted from black-white to black-transparent (with white flicker fully transparent), accuracy dropped from 99.4% to 85.0%. At this point, brain responses also changed, and calibration data became non-generalizable. Nevertheless, conditions in which one flicker remained fully transparent maintained consistency in accuracy, brain responses, and calibration generalization among themselves. Thus, performance remained stable across different opacity and scenario conditions, as long as stimulus contrast was consistent. Participants preferred more transparent conditions due to reduced visual fatigue, indicating a trade-off between accuracy and user comfort. A configuration with 100% opacity for black flicker and 50% for white provided the best performance, balancing accuracy, visual fatigue, and calibration generalization. These findings provide key insights for advancing BCI toward real-world applications.