Abstract
Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-invasive BCI systems have not successfully covered the entire alphabet, limiting their practicality. In this paper, we propose a novel non-invasive EEG-based BCI system with Curriculum-based Neural Spelling Framework, which recognizes all 26 alphabet letters by decoding neural signals associated with handwriting first, and then apply a Generative AI (GenAI) to enhance spell-based neural language decoding tasks. Our approach combines the ease of handwriting with the accessibility of EEG technology, utilizing advanced neural decoding algorithms and pre-trained large language models (LLMs) to translate EEG patterns into text with high accuracy. This system show how GenAI can improve the performance of typical spelling-based neural language decoding task, and addresses the limitations of previous methods, offering a scalable and user-friendly solution for individuals with communication impairments, thereby enhancing inclusive communication options.
Abstract (translated)
脑机接口（BCI）通过直接将神经活动转换为文本，无需物理动作，提供了一种有前景的方法。然而，现有的非侵入性 BCI 系统尚未成功覆盖整个字母表，限制了其实用性。在本文中，我们提出了一种基于课程学习的新型非侵入式 EEG（脑电图）BCI 系统，该系统首先通过解码与手写相关的神经信号来识别所有 26 个英文字母，然后应用生成人工智能（GenAI）来增强基于拼写的神经语言解码任务。我们的方法结合了书写简便性和 EEG 技术的易用性，并利用先进的神经解码算法和预训练的大规模语言模型（LLM），将 EEG 模式准确地转换为文本。该系统展示了 GenAI 如何提升典型的基于拼写式的神经语言解码任务的表现，解决了先前方法的局限性，为有沟通障碍的人提供了一种可扩展且用户友好的解决方案，从而增强了包容性的沟通选项。
URL
https://arxiv.org/abs/2501.17489
