Integrating Large Language Models and Brain Decoding for Augmented Human-Computer Interaction: A Prototype LLM-P3-BCI Speller

One of the major advances in Human-Computer Interaction has been the integration of predictive language modeling in text input interfaces. The introduction of intelligent text entry systems, proposing for instance word completion and word suggestions, made written communication faster and more efficient. Currently, intelligent text entry systems depend upon a manual selection of the intended word or phrase. Such a necessary motor act inherently impacts communication and possibly represents a system bottleneck and a source of potential errors. This required task might be even more problematic in individuals with motor disorders, where simple motor acts, if ever possible, might require high cognitive and physical effort. In this regard, Brain–Computer Interfaces (BCI) provide alternative non-muscular channels for efficient human-computer and human-machine interactions. I here present a prototype BCI system that exploits advanced predictive writing and online brain decoding to boost writt...