An Interpretable Transformer-Based Foundation Model for Cross-Procedural Skill Assessment Using Raw fNIRS Signals

Current methods largely rely on subjective observation and rating, which introduce variability and bias, lack physiological grounding, and do not generalize across tasks or individuals . While functional near - infrared spectroscopy (fNIRS) has emerged as a promising non - invasive tool for measuring neural activity during task performance [Nemani et al., 2018; Gao et al., 2021; Keles et al., 2021], prior models -- including our own -- have largely relied on extensive preprocessing pipelines, lacked physiological interpretability, and demonstrated limited generalization across tasks or stress conditions . Recent sub - region - based analyses have shown that localized activation patterns in prefrontal and motor - related areas can distinguish between proficient and non - proficient performers [Nemani et al., 2018, Gao et al., 2021, Subedi et al., 2025]. The complex spatiotemporal dynamics captured in these signals contain rich information about cognitive and motor processes that could enable more robust, generalizable assessments across diverse settings. However, existing models tend to be task - specific and require retraining when applied to new procedures, impeding scalability . These limitations highlight the need for a domain - specific foundation model -- an architecture trained to extract reusable neural representations across various tasks, individuals, and conditions. In contrast to conventional pipelines, such a model would support modular adaptation to new contexts using minimal data, while retaining interpretability. Foundation models, initially developed in natural language processing and vision [ Lu et.