Uncertainty-Aware Large Language Models for Adaptive Brain-Computer Interface Decoding
Keywords:
brain-computer interfaces; large language models; uncertainty quantification; adaptive decoding; neural engineering; trustworthy artificial intelligence; socio-technical systemsAbstract
Large language models have introduced powerful sequence priors for neural decoding, but their integration into brain-computer interfaces introduces deep uncertainties related to neural variability, model calibration, user intent, and long-term adaptation. This paper develops a system-level analysis of uncertainty-aware large language models for adaptive brain-computer interface decoding. It examines the architectural trade-offs involved in coupling neural encoders with probabilistic language models, the role of uncertainty as an internal control signal, and the infrastructure required for closed-loop adaptation. The discussion extends beyond decoding accuracy to address robustness, fairness, governance, clinical deployment, sustainability, and policy implications. A central argument is that uncertainty should not be treated merely as a residual error to be minimized, but as an informational resource for adaptive coordination between user, decoder, and interface. This perspective shifts design attention from static decoding pipelines toward continuously negotiating systems that can express confidence, defer decisions, request clarification, and update behavior under distribution shift. The paper reviews relevant progress in neural decoding and language modeling, identifies structural failure modes that emerge when opaque generative priors are introduced into assistive neurotechnology, and proposes system governance principles for safe and equitable deployment. The analysis integrates perspectives from neural engineering, machine learning, human-computer interaction, and socio-technical systems research. The result is a conceptual architecture in which calibrated uncertainty supports adaptive autonomy, preserves user agency, and enables responsible clinical translation.
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