LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910982338510848 |
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| author | Özdogan, Miran Landau, Gilad Elvers, Gereon Jayalath, Dulhan Somaiya, Pratik Mantegna, Francesco Woolrich, Mark Jones, Oiwi Parker |
| author_facet | Özdogan, Miran Landau, Gilad Elvers, Gereon Jayalath, Dulhan Somaiya, Pratik Mantegna, Francesco Woolrich, Mark Jones, Oiwi Parker |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02098 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale Özdogan, Miran Landau, Gilad Elvers, Gereon Jayalath, Dulhan Somaiya, Pratik Mantegna, Francesco Woolrich, Mark Jones, Oiwi Parker Machine Learning 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. |
| title | LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.02098 |