Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface

Area of Science:

• Neurotechnology and Artificial Intelligence
• Assistive Communication Technologies
• Human-Computer Interaction

Background:

• The convergence of artificial intelligence (AI) and neurotechnology presents significant opportunities for assistive communication.
• Non-invasive brain-computer interface (BCI) spellers are evolving, with a growing focus on predictive text capabilities.
• Large language models (LLMs) have advanced rapidly, offering sophisticated natural language processing and generation.

Purpose of the Study:

• To examine the integration of non-invasive BCI spellers with LLMs for predictive communication.
• To review the evolution of language models and their application in BCI spellers.
• To identify challenges and future directions for enhancing BCI-mediated communication.

Main Methods:

• Review of language model evolution from rule-based systems to deep learning architectures.
• Survey of existing BCI speller implementations incorporating language modeling.
• Analysis of pilot studies on LLM integration within BCI systems.

Main Results:

• LLMs enhance predictive writing capabilities within BCI spellers.
• Current BCI spellers show advancements in typing speed, accuracy, and user adaptability.
• Key challenges remain, including real-time processing, noise robustness, and neural decoding integration.

Conclusions:

• Full integration of LLMs with BCI technology promises more intuitive and effective neurotechnological communication solutions.
• Improved speed and usability of BCI-mediated communication are anticipated.
• This fusion offers a path toward advanced adaptive communication for clinical and non-clinical users.