Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading.

翻译：阅读理解作为知识获取所必需的基本认知能力，是一项复杂技能，相当数量的学习者在此领域缺乏熟练度。本研究引入了脑机接口（BCI）的创新任务，即预测个体所读单词或记号与目标推理词的相关性。我们利用最先进的大语言模型（LLMs）来引导训练中新型阅读嵌入表示。该表示通过基于注意力机制的transformer编码器整合脑电图（EEG）和眼动追踪生物标志物，在九名受试者中使用平衡样本实现了平均68.7%的五折交叉验证准确率，其中单受试者最高准确率达到71.2%。本研究开创性地整合了LLMs、EEG和眼动追踪技术，用于在单词层面预测人类阅读理解。我们对预训练的来自Transformers的双向编码器表示（BERT）模型进行微调以获取词嵌入，该模型不包含阅读任务相关信息。尽管缺乏任务特定细节，该模型仍轻松达到92.7%的准确率，从而验证了我们从LLMs中获得的发现。这项工作为开发辅助阅读工具迈出了初步步伐。