SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language

Recently, the idea of brain-model alignment has been the topic of several influential works. However, most of previous studies were based on datasets collected during regular reading tasks where the subjects were not exposed to processing linguistic incongruencies, and stimuli were not controlled for key linguistic properties. Meanwhile, interpretability studies of Large Language Models pay growing attention to thoroughly designed linguistic tasks based on certain acceptability measures. We present a dataset that contains 600 sentences with a combination of congruent and grammatically or/and semantically incongruent sentences coupled with high density 64-channel EEG recordings of 21 participants. The text stimuli were assessed by native speakers and later used in EEG recording and validation and LLM probing. The validation results proved suitability of the data for future research on brain-model alignment in the linguistic context.