Abstract
Text generating capabilities have undergone a substantial transformation with the introduction of large language models (LLMs). Electroencephalography (EEG)-based text production is still difficult, though, because it requires a lot of data and processing power. This paper introduces a new method that combines the use of the Gemma 2B LLM with a classifier-LLM architecture to incorporate a Recurrent Neural Network (RNN) encoder. Our approach drastically lowers the amount of data and compute power needed while achieving performance close to that of cutting-edge methods. Notably, compared to current methodologies, our methodology delivers an overall performance improvement of 10%. The suggested architecture demonstrates the possibility of effective transfer learning for EEG-based text production, remaining strong and functional even in the face of data limits. This work highlights the potential of integrating LLMs with EEG decoding to improve assistive technologies and improve independence and communication for those with severe motor limitations. Our method pushes the limits of present capabilities and opens new paths for research and application in brain-computer interfaces by efficiently using the strengths of pre-trained language models. This makes EEG-based text production more accessible and efficient.
Abstract (translated)
文本生成能力随着大型语言模型（LLM）的出现经历了重大变革。然而，基于脑电图（EEG）的文本生产仍然面临挑战，因为这需要大量的数据和计算资源。本文介绍了一种新的方法，该方法结合使用了Gemini 2B LLM与分类器-LLM架构，并引入了一个循环神经网络（RNN）编码器。我们的方法显著降低了所需的数据量和计算能力，同时实现了接近前沿技术的性能表现。值得注意的是，相比当前的方法，我们提出的方法总体性能提高了10%。 所提出的架构展示了基于EEG文本生成的有效迁移学习的可能性，在数据有限的情况下依然保持强大且功能完备。这项工作强调了将LLM与EEG解码相结合以改进辅助技术和提升严重运动受限人士的独立性和沟通能力的巨大潜力。我们的方法突破了现有技术的能力极限，通过充分利用预训练语言模型的优势，为脑机接口的研究和应用开辟了新的路径，从而使得基于EEG的文本生成更加可访问且高效。
URL
https://arxiv.org/abs/2509.07202
