混合式EEG驱动脑机接口：面向个性化语言康复的大语言模型框架
Hybrid EEG--Driven Brain--Computer Interface: A Large Language Model Framework for Personalized Language Rehabilitation
AI总结：
针对传统语言康复工具无法实时适配神经疾病患者认知语言需求的问题，提出结合EEG脑机接口与大语言模型的混合框架，支持意念操控、个性化内容与动态难度调整，助力语言康复。
摘要：
传统的增强与替代沟通（AAC）系统及语言学习平台往往无法实时适应用户的认知与语言需求，在中风后失语症或肌萎缩侧索硬化症等神经疾病场景中尤为突出。近期，基于无创脑电图（EEG）的脑机接口（BCI）与基于transformer的大语言模型（LLM）的研究进展展现出互补优势：BCI能以低疲劳度捕捉用户的神经意图，LLM则可生成贴合语境的语言内容。我们提出并评估了一种新型混合框架，利用实时EEG信号驱动由LLM赋能的语言康复助手。该系统旨在：（1）让有严重言语或运动障碍的用户能通过意念指令操作语言学习模块；（2）动态个性化定制词汇、造句练习与纠正反馈；（3）监测认知努力的神经标志物以实时调整任务难度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
