The brain’s ability to perform complex computations at varying timescales is crucial, ranging from understanding single words to grasping the overarching narrative of a story. Recently, multi-timescale long short-term memory (MT-LSTM) models (Mahto et al. 2020 ; Jain et al. 2020 ) have been introduced, which use temporally tuned parameters to induce sensitivity to different timescales of language processing (i.e., related to near/distant words). However, there has not been an exploration of the relationship between such temporally tuned information processing in MT-LSTMs and the brain’s processing of language using high temporal resolution recording modalities, such as electroencephalography (EEG). To bridge this gap, we used an EEG dataset recorded while participants listened to Chapter 1 of “Alice in Wonderland” and trained ridge regression models to predict the temporally tuned MT-LSTM embeddings from EEG responses. Our analysis reveals that EEG signals can be used to predict MT-LSTM embeddings across various timescales. For longer timescales, our models produced accurate predictions within an extended time window of ±2 s around word onset, while for shorter timescales, significant predictions are confined to a narrower window ranging from −180 ms to 790 ms. Intriguingly, we observed that short timescale information is not only processed in the vicinity of word onset but also at more distant time points. These observations underscore the parallels and discrepancies between computational models and the neural mechanisms of the brain. As word embeddings are used more as in silico models of semantic representation in the brain, a more explicit consideration of timescale-dependent processing enables more targeted explorations of language processing in humans and machines.