Exploring the relationship between features calculated from contextual embeddings and EEG band power during sentence reading in Chinese - Wang, Yao; Xue, Tiantian; Yang, Xingyu (Frontiers in Neuroscience, 2025)
IntroductionContextual embeddings—a core component of large language models (LLMs) that generate dynamic vector representations capturing words’ semantic properties—have demonstrated structural similarities to brain activity patterns at the single-word level. This alignment supports the theoretical framework proposing vector-based neural coding for natural language processing in the brain, where linguistic units may be represented as context-sensitive vectors analogous to LLM-derived embeddings. Building on this framework, we hypothesize that cumulative distance metrics between contextual embeddings of adjacent linguistic units (words/Chinese characters) in sentence contexts may quantitatively reflect neural activation intensity during reading comprehension.MethodsUsing large-scale EEG datasets collected during reading tasks, we systematically investigated the relationship between these computationally derived distance features and frequency-specific band power measures associated with neural activity.ResultsIn conclusion, gamma-band power exhibited associations with various NLP features in the ChineseEEG dataset, whereas no comparable gamma-specific effects were observed in the ZuCo1.0 dataset. Additionally, significant effects were found in other frequency bands for both datasets.DiscussionThe mixed yet intriguing results invite a deeper discussion of the directional associations (positive/negative) observed in Gamma and other frequency bands, their cognitive implications, and the potential influence of textual characteristics on these findings. While observed effects may be somehow text- or dataset- dependent, our analyses revealed associations between various distance metrics and neural responses, consistent with predictions derived from the vector-based neural coding framework.