# MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing

arXiv: https://arxiv.org/abs/2208.11488 · HTML: https://mlsanity.com/paper/2208.11488/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Disentangling Syntax and Semantics in the Brain with Deep Networks](https://mlsanity.com/paper/2103.01620/) (2021)
- [Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech](https://mlsanity.com/paper/2103.01032/) (2021)
- [Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects](https://mlsanity.com/paper/2110.06078/) (2021)
- [Long-range and hierarchical language predictions in brains and algorithms](https://mlsanity.com/paper/2111.14232/) (2021)
- [Tracking Naturalistic Linguistic Predictions with Deep Neural Language Models](https://mlsanity.com/paper/1909.04400/) (2019)
- [Deep Recurrent Encoder: A scalable end-to-end network to model brain signals](https://mlsanity.com/paper/2103.02339/) (2021)
- [Bridging LSTM Architecture and the Neural Dynamics during Reading](https://mlsanity.com/paper/1604.06635/) (2016)

## Built on by (most cited first, 2 of 2)

- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)

## Abstract (arXiv)

The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked related fields such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.