  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.
