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.

翻译：脑机接口（BCIs）通过将神经活动直接转化为文本，消除了对物理动作的需求，展现了一条前景广阔的途径。然而，现有的非侵入式脑机接口系统尚未成功覆盖全部字母表，限制了其实用性。在本文中，我们提出了一种新颖的基于脑电图（EEG）的非侵入式脑机接口系统，该系统采用基于课程学习的神经拼写框架，首先通过解码与手写相关的神经信号来识别全部26个字母，然后应用生成式人工智能（GenAI）来增强基于拼写的神经语言解码任务。我们的方法结合了手写的简易性与脑电图技术的可及性，利用先进的神经解码算法和预训练的大语言模型（LLMs），将脑电图模式高精度地转化为文本。该系统展示了生成式人工智能如何提升典型基于拼写的神经语言解码任务的性能，解决了先前方法的局限性，为存在沟通障碍的个体提供了一个可扩展且用户友好的解决方案，从而增强了包容性沟通的选择。