Retrieving Event-related Human Brain Dynamics from Natural Sentence Reading

TL;DR

This study analyzes EEG signals during natural sentence reading to identify brain dynamics related to language comprehension, revealing specific temporal and spectral patterns associated with semantic processing.

Contribution

It introduces the first analysis of ERPs and ERSPs on benchmark EEG and eye-tracking data during natural reading, linking brain activity to language understanding.

Findings

Peak brain activity at ~162 ms in occipital region

Semantic responses occur around 200 ms across EEG bands

High gamma increases, low beta decreases at ~200 ms

Abstract

Electroencephalography (EEG) signals recordings when people reading natural languages are commonly used as a cognitive method to interpret human language understanding in neuroscience and psycholinguistics. Previous studies have demonstrated that the human fixation and activation in word reading associated with some brain regions, but it is not clear when and how to measure the brain dynamics across time and frequency domains. In this study, we propose the first analysis of event-related brain potentials (ERPs), and event-related spectral perturbations (ERSPs) on benchmark datasets which consist of sentence-level simultaneous EEG and related eye-tracking recorded from human natural reading experiment tasks. Our results showed peaks evoked at around 162 ms after the stimulus (starting to read each sentence) in the occipital area, indicating the brain retriving lexical and semantic visual information processing approaching 200 ms from the sentence onset. Furthermore, the occipital ERP around 200ms presents negative power and positive power in short and long reaction times. In addition, the occipital ERSP around 200ms demonstrated increased high gamma and decreased low beta and low gamma power, relative to the baseline. Our results implied that most of the semantic-perception responses occurred around the 200ms in alpha, beta and gamma bands of EEG signals. Our findings also provide potential impacts on promoting cognitive natural language processing models evaluation from EEG dynamics.