Carìa Andrea
Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.
Sensors (Basel). 2025 Jun 26;25(13):3987. doi: 10.3390/s25133987.
Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.
先进的人工智能与神经技术的融合为辅助通信带来了变革性潜力。这篇观点文章探讨了非侵入性脑机接口（BCI）拼写器与大语言模型（LLM）之间正在出现的融合，重点关注为有运动或语言障碍的个体进行预测性通信。首先，我将回顾语言模型的演变——从早期基于规则的系统到当代深度学习架构——以及它们在增强预测性写作方面的作用。其次，我将调查结合语言建模的BCI拼写器的现有实现，并突出最近探索将LLM集成到BCI中的试点研究。第三，我将研究尽管在打字速度、准确性和用户适应性方面取得了进展，但LLM与BCI拼写器的融合仍然面临实时处理、抗噪声能力以及神经解码输出与概率语言生成框架的集成等关键挑战。最后，我将讨论LLM与BCI技术的全面集成如何能够大幅提高BCI介导通信的速度和可用性，为临床和非临床用户提供一条通往更直观、自适应和有效神经技术解决方案的途径。
