Personalized Brain Language Models for Context-Aware Neural Signal Interpretation

The efficacy of Brain–Computer Interfaces (BCIs) is often limited by the inherent variability in neural representations across individuals and the generalized nature of current decoding models. While significant progress has been made in signal acquisition [Neuroba Research (2026a)], neural decoding [Neuroba Research (2026b)], and secure transmission [Neuroba Research (2026c)], a critical gap remains in achieving truly personalized and context-aware semantic interpretation of neural signals. Existing approaches often struggle with the inter-subject variability of electroencephalography (EEG) signals and the contextual ambiguity inherent in brain activity, leading to low generalization and limited semantic understanding. This paper introduces the Neuroba Personalized Brain Language Model (PBLM) Framework, a novel approach designed to bridge this gap by integrating subject-specific neural representations with dynamic contextual information to enable semantic-level interpretation. The PBLM framework comprises modules for neural signal encoding, subject-specific representation learning, context embedding, semantic neural mapping, and adaptive learning. Key contributions include a modular architecture for personalized neural interpretation, mathematical formulations for personalization and context integration, and a discussion of real-time implementation considerations. While PBLM offers a promising direction for enhancing BCI performance, challenges such as dataset scarcity, computational constraints, and ethical implications of neural personalization require further investigation. This framework aligns with Layer 04 (INTERPRET) of the Neuroba NCTS Framework, providing a crucial step towards more intuitive and adaptive neuro-AI systems.