Language Models (LMs) are primarily developed to address different NLP tasks, and are not intended to reflect human reading comprehension processing. However, since these models are trained with the same written materials that humans also process in reading, they must share many of the same abilities in the reading comprehension processes as a human, particularly in next-word prediction ability. Comparing the accuracy of different types of LMs and humans in the next-word prediction task demonstrates their predictive capabilities but falls short of confirming whether LMs and the human brain process reading in a similar manner. This project aims to address this challenge by using electroencephalography (EEG) signals to examine next-word predictability of different LMs during reading.