Towards Predictive Communication with Brain-Computer Interfaces integrating Large Language Models

Abstract: This perspective article aims at providing an outline of the state of the art and future developments towards the integration of cutting-edge predictive LLMs with BCI. A synthetic overview of early and more recent linguistic models, from NLP models to recent LLM, that to a varying extent improved predictive writing systems, is first provided. Second, a summary of previous BCI implementations integrating LLMs is presented. The few preliminary studies investigating the possible combination of LLM with BCI spellers to efficiently support fast communication and control are then described. Finally, current challenges and limitations towards the full integration of LLM with BCI systems are discussed. Recent investigations suggest that the combination of LLM with BCI might drastically improve human-computer interaction in patients with motor or language disorders as well as in healthy individuals. In particular, the pretrained autoregressive transformer models, such as GPT, that capitalize from parallelization, learning through pre-training and fine-tuning, promise a substantial improvement of BCI for communication with respect to previous systems incorporating simpler LLMs. Indeed, among various models, the GPT-2 was shown to represent an excellent candidate for its integration into BCI although testing was only perfomed on simulated conversations and not on real BCI scenarios. Prospectively, the full integration of LLM with advanced BCI systems might lead to a big leap forward towards fast, efficient and user-adaptive neurotechnology.