LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale

• URL: http://arxiv.org/abs/2506.02098v1
• Date: Mon, 02 Jun 2025 17:59:41 GMT
• Title: LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
• Authors: Miran Özdogan, Gilad Landau, Gereon Elvers, Dulhan Jayalath, Pratik Somaiya, Francesco Mantegna, Mark Woolrich, Oiwi Parker Jones,
• Abstract summary: LibriBrain is the largest single-subject MEG dataset to date for speech decoding.<n>This unprecedented depth' of within-subject data enables exploration of neural representations at a scale previously unavailable with non-invasive methods.
• Score: 2.225053366951265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5$\times$ larger than the next comparable dataset and 50$\times$ larger than most. This unprecedented `depth' of within-subject data enables exploration of neural representations at a scale previously unavailable with non-invasive methods. LibriBrain comprises high-quality MEG recordings together with detailed annotations from a single participant listening to naturalistic spoken English, covering nearly the full Sherlock Holmes canon. Designed to support advances in neural decoding, LibriBrain comes with a Python library for streamlined integration with deep learning frameworks, standard data splits for reproducibility, and baseline results for three foundational decoding tasks: speech detection, phoneme classification, and word classification. Baseline experiments demonstrate that increasing training data yields substantial improvements in decoding performance, highlighting the value of scaling up deep, within-subject datasets. By releasing this dataset, we aim to empower the research community to advance speech decoding methodologies and accelerate the development of safe, effective clinical brain-computer interfaces.

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