Towards an End-to-End Framework for Invasive Brain Signal Decoding with Large Language Models

TL;DR

This paper presents an innovative end-to-end framework utilizing large language models to decode invasive brain signals for speech neuroprosthesis, demonstrating promising results and paving the way for future BCI advancements.

Contribution

Introduces a novel end-to-end decoding framework that integrates large language models, advancing speech neuroprosthesis technology beyond traditional cascade models.

Findings

Achieves results comparable to state-of-the-art cascade models

Demonstrates the potential of LLMs in decoding complex neural signals

Highlights the future potential of E2E frameworks in BCI applications

Abstract

In this paper, we introduce a groundbreaking end-to-end (E2E) framework for decoding invasive brain signals, marking a significant advancement in the field of speech neuroprosthesis. Our methodology leverages the comprehensive reasoning abilities of large language models (LLMs) to facilitate direct decoding. By fully integrating LLMs, we achieve results comparable to the state-of-the-art cascade models. Our findings underscore the immense potential of E2E frameworks in speech neuroprosthesis, particularly as the technology behind brain-computer interfaces (BCIs) and the availability of relevant datasets continue to evolve. This work not only showcases the efficacy of combining LLMs with E2E decoding for enhancing speech neuroprosthesis but also sets a new direction for future research in BCI applications, underscoring the impact of LLMs in decoding complex neural signals for communication restoration. Code will be made available at https://github.com/FsFrancis15/BrainLLM.