Brain encoding is the process of mapping stimuli to brain activity. There is a vast literature on linguistic brain encoding for functional MRI (fMRI) related to syntactic and semantic representations. Magnetoencephalography (MEG), with higher temporal resolution than fMRI, enables us to look more precisely at the timing of linguistic feature processing. Unlike MEG decoding, few studies on MEG encoding using natural stimuli exist. Existing ones on story listening focus on phoneme and simple word-based features, ignoring more abstract features such as context, syntactic and semantic aspects. Inspired by previous fMRI studies, we study MEG brain encoding using basic syntactic and semantic features, with various context lengths and directions (past vs. future), for a dataset of 8 subjects listening to stories. We find that BERT representations predict MEG significantly but not other syntactic features or word embeddings (e.g. GloVe), allowing us to encode MEG in a distributed way across auditory and language regions in time. In particular, past context is crucial in obtaining significant results.