Connecting a P300 speller to a large language model

Abstract

The advent of large-language models (LLMs) offers a transformative approach for improving the performance of brain-computer interface (BCI) spellers. We propose a novel framework that leverages the contextual understanding of LLMs to compensate for imperfect BCI decoding. Using existing P300 speller data, we simulated a system where users select letters to form words, generating text with characteristic spelling errors. This output is then processed by an LLM, which corrects the errors - a task that becomes more effective when the model considers full-sentence context. Our findings suggest that this synergy can accelerate communication rates by relaxing the need for high single-character accuracy. Beyond speed, integrating an LLM transforms the BCI into an intelligent agent, capable of acting as a discussant and assistant, thereby enriching the user experience.