This perspective article aims at providing an outline of the state of the art and future developments towards the integration of cutting-edge predictive language models with BCI. A synthetic overview of early and more recent linguistic models, from natural language processing (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 language models 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 language models. 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.

翻译：本文旨在概述将前沿预测性语言模型与脑机接口（BCI）集成的现状与未来发展前景。首先，我们系统性地综述了从自然语言处理（NLP）模型到近期大语言模型（LLM）的早期及最新语言模型，这些模型在不同程度上改进了预测性书写系统。其次，我们总结了以往集成语言模型的BCI实施方案。随后，描述了为数不多的探索将LLM与BCI拼写器结合以有效支持快速通信与控制的初步研究。最后，我们讨论了LLM与BCI系统完全融合所面临的当前挑战与局限。近期研究表明，LLM与BCI的结合可能极大改善运动或语言障碍患者以及健康个体的人机交互体验。特别是基于预训练自回归Transformer架构的模型（如GPT），其充分利用并行化、预训练与微调学习机制，有望相对于以往集成简单语言模型的系统，在通信BCI方面实现显著提升。事实上，在多种模型中，GPT-2已被证明是集成至BCI的优异候选模型，尽管目前测试仅基于模拟对话而未在真实BCI场景中进行。展望未来，LLM与先进BCI系统的深度融合，可能为实现快速、高效且用户自适应的神经技术带来重大飞跃。