用脑电波写字，再用AI把字变文字，实现全字母输入。
Neural Spelling: A Spell-Based BCI System for Language Neural Decoding
- 通过手写想象解码脑电信号，识别全部26个字母。
- 结合生成式AI，使拼写型语言解码准确率显著提升。
- 适合沟通障碍者使用，操作简单且可扩展性强。
脑机接口（BCI）通过直接将神经活动转化为文本，为无需肢体动作的交流提供可能。然而，现有非侵入式BCI系统尚未覆盖全部26个英文字母，限制了实际应用。本文提出一种基于课程学习的神经拼写框架（Curriculum-based Neural Spelling Framework），利用非侵入式脑电图（EEG）技术，通过解码与书写相关的神经信号，识别全部26个英文字母，并引入生成式人工智能（GenAI）增强拼写型神经语言解码任务。该方法融合手写意念与EEG技术优势，结合先进的神经解码算法和预训练大语言模型（LLMs），将脑电模式高精度转为文本。实验表明，生成式AI显著提升了典型拼写型神经语言解码性能，克服了以往方法的局限性，为沟通障碍人群提供了可扩展、易用的包容性通信解决方案。
原文摘要 · Abstract (English)
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.
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