迈向基于大型语言模型的侵入式脑信号解码端到端框架
Towards an End-to-End Framework for Invasive Brain Signal Decoding with Large Language Models
AI总结：
本文提出一个端到端框架，利用大型语言模型的推理能力直接解码侵入式脑信号，取得了媲美最先进级联模型的效果，为语音神经假体和脑机接口研究指明了新方向。
摘要：
在本文中，我们引入了一个开创性的端到端（E2E）框架用于解码侵入式脑信号，标志着语音神经假体领域的重大进展。我们的方法利用大型语言模型（LLMs）的综合推理能力来促进直接解码。通过充分整合LLMs，我们取得了与最先进级联模型相媲美的结果。我们的发现强调了E2E框架在语音神经假体中的巨大潜力，特别是随着脑机接口（BCIs）背后的技术和相关数据集的可用性不断发展。这项工作不仅展示了将LLMs与E2E解码相结合以增强语音神经假体的有效性，而且为BCI应用的未来研究设定了新方向，强调了LLMs在解码复杂神经信号以恢复通信方面的影响。代码将发布于https://github.com/FsFrancis15/BrainLLM。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
