用脑电控制大模型，为失语患者实时定制语言康复方案。
Hybrid EEG--Driven Brain--Computer Interface: A Large Language Model Framework for Personalized Language Rehabilitation
- 脑电信号驱动大模型生成个性化语言训练内容。
- 可动态调整词汇难度与反馈，提升康复效率。
- 适合中风或渐冻症等神经疾病患者语言康复使用。
传统辅助沟通系统与语言学习平台难以实时适应用户认知与语言需求，尤其在中风后失语症或肌萎缩侧索硬化症等神经疾病中表现不佳。非侵入式脑电图（EEG）脑机接口与基于Transformer的大语言模型（LLMs）各具优势：前者以低疲劳度捕捉用户神经意图，后者可生成情境适配的语言内容。本文提出并评估了一种新型混合框架，利用实时脑电信号驱动大模型语言康复助手。该系统旨在：(1) 让严重言语或运动障碍者通过意念命令操作语言学习模块；(2) 动态个性化词汇、句式练习与纠错反馈；(3) 监测认知负荷的神经标记，实时调节任务难度。
原文摘要 · Abstract (English)
Conventional augmentative and alternative communication (AAC) systems and language-learning platforms often fail to adapt in real time to the user's cognitive and linguistic needs, especially in neurological conditions such as post-stroke aphasia or amyotrophic lateral sclerosis. Recent advances in noninvasive electroencephalography (EEG)--based brain-computer interfaces (BCIs) and transformer--based large language models (LLMs) offer complementary strengths: BCIs capture users' neural intent with low fatigue, while LLMs generate contextually tailored language content. We propose and evaluate a novel hybrid framework that leverages real-time EEG signals to drive an LLM-powered language rehabilitation assistant. This system aims to: (1) enable users with severe speech or motor impairments to navigate language-learning modules via mental commands; (2) dynamically personalize vocabulary, sentence-construction exercises, and corrective feedback; and (3) monitor neural markers of cognitive effort to adjust task difficulty on the fly.
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