This research tackles key challenges in Brain-Computer Interface (BCI) systems. Non-invasive EEG-based BCIs are the most widely used due to their safety and accessibility, holding nearly 60% of the market in 2024. However, they face persistent barriers: low signal-to-noise ratios caused by skull attenuation and unrelated brain activity, and strong non-stationarity both within and across users, requiring repeated calibration. Further obstacles include limited publicly available datasets for training modern models, constrained usability due to complex acquisition protocols and a lack of broadly applicable real-world use cases. Together, these issues restrict the EEG-based BCIs from achieving the ubiquity of modalities such as speech or text. To overcome these limitations, we adopt a systems-level approach, viewing BCI challenges as interconnected subproblems within the broader ecosystem of signal acquisition, decoding, generalization, and usability. This perspective emphasizes exploiting resources rather than working in isolation, like data from other users, predictions from existing models, or mutual information from complementary neural signals. To promote generalization and overcome data scarcity, we employ data-efficient techniques such as few-shot learning, active learning, meta-learning, optimal transport, etc. We also extend this principle to multi-human learning, where predictions from diverse users are aggregated to stabilize error-related potential (ErrP) classification. Beyond human resources, we introduce Response Coupling, an approach that integrates two neural signals, leveraging their mutual information to enhance accuracy, suppress noise, improve synchronization, and enable unsupervised signal quality estimation. Finally, we explore resource-aware zero-shot learning across signals with preliminary results and talk about current challenges and future research opportunities. Collectively, these contributions highlight how systematically identifying and exploiting resources leads to more robust, generalizable, and practical BCIs, moving beyond incremental improvements toward versatile real-world systems.