P۳۰۰-Based Machine Learning BCIs for Brain-to-Text Neural Decoding in Paralysed Patients: A Narrative Review

This narrative review synthesizes recent advances in brain-to-text neural decoding using P۳۰۰-based machine learning brain-computer interfaces (BCIs) for individuals with severe motor impairments. It situates the P۳۰۰ event-related potential within its neurophysiological and theoretical foundations and traces its trajectory from early speller paradigms to contemporary adaptive, hybrid, and deep learning-driven systems. Historical milestones in stimulus presentation, signal processing, and classifier design are examined alongside recent innovations that integrate multimodal ERP features, probabilistic models, and closed-loop neurofeedback to enhance accuracy, robustness, and ecological validity. Clinical applications extend beyond communication restoration to neurorehabilitation, encompassing motor recovery and language training, while real-world deployment increasingly emphasizes portability, cost-effectiveness, and home usability. Critical reflections highlight persistent challenges, including inter-subject variability in P۳۰۰ morphology, trade-offs between interpretability and accuracy, and underexplored ethical issues related to privacy, agency, and equitable access. The review concludes by framing P۳۰۰-based brain-to-text BCIs as evolving from static laboratory tools into intelligent, context-aware systems capable of co-adapting with users and shaping the clinical, societal, and economic landscape of assistive neurotechnology.

کلیدواژه ها:

Adaptive machine learning ، Assistive neurotechnology ، Brain-computer interface (BCI) ، P۳۰۰ event related potential ، Paralysed patient communication ، Neural decoding