nm000229
Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
ANAT MEG
MEG-MASC is a high-quality magnetoencephalography dataset comprising raw MEG recordings from 27 English speakers listening to approximately two hours of naturalistic stories from the Manually Annotated Sub-Corpus (MASC). The dataset includes precise temporal annotations of word and phoneme onsets/offsets, organized according to the Brain Imaging Data Structure (BIDS) standard. This benchmark dataset enables large-scale encoding and decoding analyses of neural responses to natural speech processing, with accompanying code for validation analyses including temporal decoding of phonetic features and word frequency effects.
- Participants
- 27
- Citations
- 42
- Size
- 99.3 GB
- Version
- vv1.0.1
- Updated
- Jul 10, 2026