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）通过直接将神经活动转化为文本，消除了对物理动作的需求，展现出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，通过首先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便捷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），以高精度将脑电图模式转化为文本。该系统展示了GenAI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。