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The large-scale, real-world deployment of brain–computer interface (BCI) is confronted with human factors bottlenecks, including reliability and usability. While modern human factors engineering has encompassed domains such as cognitive ergonomics, workload, adaptive automation, and human-in-the-loop systems, its theories and methodologies have historically placed their main emphasis on overt behavior and peripheral physiological measures, rather than on central neural activity as a primary control input. For the emerging interaction paradigm that takes central nervous activity as direct input and emphasizes long-term brain–computer co-adaptation, there remains a shortage of targeted conceptual and methodological frameworks. As a conceptual review, this paper discusses the necessity and disciplinary foundations for developing Human Factors Engineering for BCIs as an emerging interdisciplinary research direction. First, grounded in the defining features of BCI interaction—such as the implicit nature of input and brain–computer co-adaptation—it delineates the research boundaries and summarizes the core research objects as user neurocognitive characteristics, brain–computer collaborative adaptation mechanisms, multimodal feedback regulation, and contextual adaptation patterns. Second, it identifies three core scientific questions: individual differences and dynamic adaptation, human factors optimization and fault tolerance, and contextual generalizability principles, with ethics, privacy, and user rights and interests as an overarching dimension. Furthermore, it constructs a methodological system consisting of multimodal measurement and modeling, closed-loop validation, and multidimensional evaluation, and examines the supporting role of artificial intelligence approaches within this framework. Centered on the collaborative adaptation across neural, cognitive, and system levels, Human Factors Engineering for BCIs represents a deepening of the human-centered design philosophy. It provides theoretical reference for user-friendly BCI design and extends the research boundaries of human factors engineering.
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Yan et al. (2026) studied this question.
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