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

arXiv: https://arxiv.org/abs/2506.02098 · HTML: https://mlsanity.com/paper/2506.02098/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 62 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [SEANet: A Multi-modal Speech Enhancement Network](https://mlsanity.com/paper/2009.02095/) (2020)
- [Are EEG-to-Text Models Working?](https://mlsanity.com/paper/2405.06459/) (2024)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Decoding individual words from non-invasive brain recordings across 723 participants](https://mlsanity.com/paper/2412.17829/) (2024)
- [NeuSpeech: Decode Neural signal as Speech](https://mlsanity.com/paper/2403.01748/) (2024)
- [Group-level Brain Decoding with Deep Learning](https://mlsanity.com/paper/2205.14102/) (2022)
- [NeuGPT: Unified multi-modal Neural GPT](https://mlsanity.com/paper/2410.20916/) (2024)
- [Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordings](https://mlsanity.com/paper/2410.19986/) (2024)

## Built on by (most cited first, 20 of 20)

- [The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset](https://mlsanity.com/paper/2506.10165/) (2025)
- [MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training](https://mlsanity.com/paper/2602.02494/) (2026)
- [LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale](https://mlsanity.com/paper/2608.25204/) (2026)
- [MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification](https://mlsanity.com/paper/2512.01443/) (2025)
- [Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset](https://mlsanity.com/paper/2510.21038/) (2025)
- [Zero-Shot Imagined Speech Decoding via Imagined-to-Listened MEG Mapping](https://mlsanity.com/paper/2605.08075/) (2026)
- [A Common Measure of Communication for Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.02887/) (2026)
- [The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100](https://mlsanity.com/paper/2609.03231/) (2026)
- [The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding](https://mlsanity.com/paper/2609.10296/) (2026)
- [From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining](https://mlsanity.com/paper/2512.15830/) (2025)
- [MEGState: Phoneme Decoding from Magnetoencephalography Signals](https://mlsanity.com/paper/2512.17978/) (2025)
- [SHINE: Sequential Hierarchical Integration Network for EEG and MEG](https://mlsanity.com/paper/2602.23960/) (2026)
- [MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data](https://mlsanity.com/paper/2602.18253/) (2026)
- [MEBM-Phoneme: Multi-scale Enhanced BrainMagic for End-to-End MEG Phoneme Classification](https://mlsanity.com/paper/2603.02254/) (2026)
- [MEBM-Speech: Multi-scale Enhanced BrainMagic for Robust MEG Speech Detection](https://mlsanity.com/paper/2603.02255/) (2026)
- [Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shift](https://mlsanity.com/paper/2604.04129/) (2026)
- [MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priors](https://mlsanity.com/paper/2605.31173/) (2026)
- [A Roadmap for MEG Foundation Models](https://mlsanity.com/paper/2609.04461/) (2026)
- [From Neurons to Conversation: Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.36736/) (2026)
- [Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2609.40359/) (2026)

## Abstract (arXiv)

LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5× larger than the next comparable dataset and 50× 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.